Pre-processing and differential gene expression analysis of RAW data
Ana BVA
9 luglio 2020
Introduction
Aim: To pre-process raw data from GEO using RMA and to perform a differential gene expression analysis comparing COPD vs CONTROL group
COPD
Chronic Obstructive Pulmonary Disease (COPD) is characterized by emphysema and chronic bronchitis, it’s diagnosed using spirometry and clinical information which lead to a heterogeneous COPD patients. The ranking of non commutable diseases from the WHO estimates that COPD is in the top of mortality causes and tobacco is the main risk factor but different genetic variants have been associated with this disease.
Different researchers have analyzed COPD transcriptomics using high-throughput data such as microarrays and RNA-seq. We belive it would be relevant to unravel a robust gene expression signature for COPD patients regardless if it is from different experiments or laboratories.
Background
This script is part of an analysis performed with PulmonDB data that looks to determine a common deferentially expressed genes. The analysis was divided into different vignettes computing different steps. In this script out objective is to do a meta-analysis with raw data downloaded from GEO. The results will be used as a reference and to compare them with a meta-analysis computed with PulmonDB sample contrasts.
We want to pre-process and re-analyze transcriptomic experiments from GEO that have:
Lung tissue samples
COPD vs CONTROL group
Input
This script needs the following files:
Data 1: Table of GSE experiments
Data 2: Raw data (.CEL, .TXT)
Setup
All the data has already been downloaded in the cluster 10.200.0.42, for accessing:
ssh ana@10.200.0.42
cd /home/ana/R-projects/Meta-analysis_COPDFor running the script, type:
nohup R -e "rmarkdown::render('vignettes/RMA_DE-23-June-2020.Rmd')" &The script can be found in: /home/ana/R-projects/Meta-analysis_COPD/vignettes
setwd("..")
PATH = getwd()
DATA_DIR = file.path(PATH,"data")
OUTPUT_DIR = file.path(PATH,"output_data")
FIG_DIR = file.path(PATH,"fig")
TODAY = Sys.Date()
knitr::knit_hooks$set(timeit = local({
now = NULL
function(before, options) {
if (before) {
now <<- Sys.time()
} else {
runtime = difftime(Sys.time(), now)
now <<- NULL
# use options$label if you want the chunk label as well
paste('Time for this code chunk:', as.character(runtime))
}
}})
)
knitr::opts_knit$set(root.dir = PATH)
knitr::opts_chunk$set(echo = TRUE,
timeit=TRUE,
warning=FALSE,
attr.output='style="max-height: 500px;"')And the analysis is run in: /home/ana/R-projects/Meta-analysis_COPD
Libraries
if (!requireNamespace("BiocManager", quietly = TRUE)) {
install.packages("BiocManager")
}
packages <- c("knitr",
"oligo",
"tidyverse",
"limma",
"SummarizedExperiment",
"GEOquery",
"DESeq2",
"org.Hs.eg.db",
"AnnotationDbi",
"recount")
for(l in packages){
if (!requireNamespace(l, quietly = TRUE)) {
BiocManager::install(l)}
}Time for this code chunk: 13.9964439868927
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Experiments
We selected 7 experiments that are in PulmonDB and are lung samples from COPD patients and that also have a control group to compare. These experiments are described in the following table:
gse_table <- read.csv(file.path(DATA_DIR,"GSE_table.csv"), row.names = 1)
kable(gse_table, caption = "GSE information")| Samples | Platform | Platform.manufacturer | Year | Category | |
|---|---|---|---|---|---|
| GSE1122 | 15 | [Hu6800] | Affymetrix | 2004 | Normal lung & Emphysema & AAD |
| GSE1650 | 30 | [HG-U133A] | Affymetrix | 2004 | Normal or mild Emphysema & severe Emphysema |
| GSE27597 | 72 | [HuEx-1_0-st] | Affymetrix | 2011 | Normal lung & COPD and Emphysema |
| GSE37768 | 38 | [HG-U133_Plus_2] | Affymetrix | 2016 | Normal lung & COPD |
| GSE47460 | 582 | Agilent-014850 | Agilent | 2013 | Normal lung & COPD & ILD |
| GSE57148 | 189 | Illumina HiSeq 2000 | Illumina | 2015 | Normal lung & COPD |
| GSE8581 | 58 | [HG-U133_Plus_2] | Affymetrix | 2008 | Normal lung & COPD |
Time for this code chunk: 0.0177333354949951
The experiment GSE57148 is a RNA-seq experiment, and we do not need to normalize the data using RMA but we will download counts from ReCount2.
Local functions
rawCEL_normCEL: Pre-process raw data
We first read and pre-process raw .CEL files. Each experiment has it own folder with raw data per sample. Then we normalized using RMA algorithm and finally, we save it in a .CSV file.
In this function you need:
Input: GSE ID
Output: Samples normalized, boxplots and histograms from raw and normalized data
rawCEL_normCEL <- function(gse){
# select CEL files
celfiles <- list.celfiles(file.path(DATA_DIR,"celfiles",gse), full.names=TRUE,listGzipped=TRUE)
# read CEL files in R
rawData <- read.celfiles(celfiles)
#### Figures of raw data
#pdf(str_c("raw_",gse,"_boxplot",TODAY,".pdf"))
## boxplot of raw data
boxplot(rawData,target="core")
## hist of raw data
hist(rawData,target="core")
#dev.off()
## RMA normalization
normData <- rma(rawData)
#### Figures of Normalized data
#pdf(str_c("norm_",gse,"_boxplot",TODAY,".pdf"))
## boxplot of norm data
boxplot(normData)
## hist of norm data
hist(normData)
# dev.off()
#write.csv(exprs(normData),str_c(OUTPUT_DIR,"/",gse,"_normData",TODAY,".txt"),quote=F)
return(normData)
}
#sapply(tissue,rawCEL_normCEL)Time for this code chunk: 0.00420188903808594
get_GEO: get annotation using GEOquery package
This function download the ExpressionSet object from GEOquery that has the sample annotations, then we replace the expression values with our calculated pre-processed data.
Input: GSE ID, norm object with pre-processed values
Output: ExpressionSet object with GEOquery annotation and pre-processed values
get_GEO <- function(gse,norm,i=1){
qx <- getGEO(gse)
message("Data downloaded from GEOquery:")
print(qx)
if (length(qx) == 1) {
qx <- qx[[1]]
} else{
qx <- qx[i][[1]]
}
message("Colnames of GEOquery object:")
print(colnames(qx)[1:5])
message("Colnames of calculated pre-processed data:")
print(colnames(norm)[1:5])
# Rename sample columns (Change GSM18403.CEL.gz to GSM18403)
# sort(colnames(norm1))
colnames(norm) = colnames(qx)
exprs(norm)[1:3,1:3]
exprs(qx) <- exprs(norm)
return(qx)
}Time for this code chunk: 0.00354647636413574
DE: Differential expression analysis
Using this funtion, we get a table with differential expression gene results using limma package for fitting a linear model to get genes differentially expressed between a “Control” and a “COPD” group.
Input: GSE ID, optional: colCOPD is the column name in which the information of disease status can be found, coeff will show results of contrast with coeffitient found in possiton 2
Output: Table of differential expression results with all genes
DE <- function(ExpressionSet,colCOPD="Disease",coeff= 2){
# it creates the design matrix and performs limma
fit <- lmFit(ExpressionSet, model.matrix(as.formula(paste("~ 1 +", colCOPD)),
data = pData(ExpressionSet)))
# eBayes in lmFit model
ebf <- eBayes(fit)
print(colnames(coef(fit)))
# It gets the genes with the p-values
volcanoplot(ebf,coef = coeff,highlight=20, pch=20)
res <- topTable(ebf, number = Inf, p.value = 1, coef = coeff,confint=T)
# It formats in a tibble
res <- as_tibble(res,rownames="rownames")
}Time for this code chunk: 0.00349020957946777
GSE1122
This experiment evaluates gene expression profiles of emphysema using “usual” emphysema and Alpha-1 Antitrypsin Deficiency-related emphysema (AAD). As a control group, authors used normal lung tissue from “organs donated for transplant, but unused due to age or size mismatch”, non of those individuals were smokers or reported clinical airflow limitation.
This study showed that inflammation, immune responses, and proteolysis are emphysema characteristics. They also found similarities and differences between AAD and “usual” emphysema.
The authors measured 15 lung samples, 5 controls, 5 “usual” emphysema and 5 AAD emphysema. The raw data is in cel files, the platform used is [Hu6800] Affymetrix. The study was perfomed in Colorado, USA.
Pre-process raw data
We pre-processed raw data using the function rawCEL_normCEL, plots will be shown as additional output.
gse1<- rownames(gse_table)[1]
norm1 <- rawCEL_normCEL(gse1)## Loading required package: pd.hu6800## Loading required package: RSQLite## Loading required package: DBI## Platform design info loaded.## Reading in : /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE1122/GSM18403.CEL.gz
## Reading in : /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE1122/GSM18404.CEL.gz
## Reading in : /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE1122/GSM18405.CEL.gz
## Reading in : /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE1122/GSM18406.CEL.gz
## Reading in : /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE1122/GSM18407.CEL.gz
## Reading in : /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE1122/GSM18408.CEL.gz
## Reading in : /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE1122/GSM18409.CEL.gz
## Reading in : /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE1122/GSM18410.CEL.gz
## Reading in : /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE1122/GSM18411.CEL.gz
## Reading in : /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE1122/GSM18412.CEL.gz
## Reading in : /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE1122/GSM18413.CEL.gz
## Reading in : /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE1122/GSM18414.CEL.gz
## Reading in : /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE1122/GSM18415.CEL.gz
## Reading in : /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE1122/GSM18416.CEL.gz
## Reading in : /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE1122/GSM18417.CEL.gz
## Background correcting
## Normalizing
## Calculating Expression
norm1## ExpressionSet (storageMode: lockedEnvironment)
## assayData: 7129 features, 15 samples
## element names: exprs
## protocolData
## rowNames: GSM18403.CEL.gz GSM18404.CEL.gz ... GSM18417.CEL.gz (15
## total)
## varLabels: exprs dates
## varMetadata: labelDescription channel
## phenoData
## rowNames: GSM18403.CEL.gz GSM18404.CEL.gz ... GSM18417.CEL.gz (15
## total)
## varLabels: index
## varMetadata: labelDescription channel
## featureData: none
## experimentData: use 'experimentData(object)'
## Annotation: pd.hu6800Time for this code chunk: 11.0009229183197
Get annotation
We used GEOquery package to obtain sample annotations and our previous calculated pre-processed values to create an ExpressionSet object.
# get annotation using GEOquery package
geo1 <- get_GEO(gse1,norm1)## Found 1 file(s)## GSE1122_series_matrix.txt.gz## Parsed with column specification:
## cols(
## ID_REF = col_character(),
## GSM18403 = col_double(),
## GSM18404 = col_double(),
## GSM18405 = col_double(),
## GSM18406 = col_double(),
## GSM18407 = col_double(),
## GSM18408 = col_double(),
## GSM18409 = col_double(),
## GSM18410 = col_double(),
## GSM18411 = col_double(),
## GSM18412 = col_double(),
## GSM18413 = col_double(),
## GSM18414 = col_double(),
## GSM18415 = col_double(),
## GSM18416 = col_double(),
## GSM18417 = col_double()
## )## File stored at:## /tmp/Rtmp4ZNwux/GPL80.soft## Data downloaded from GEOquery:## $GSE1122_series_matrix.txt.gz
## ExpressionSet (storageMode: lockedEnvironment)
## assayData: 7129 features, 15 samples
## element names: exprs
## protocolData: none
## phenoData
## sampleNames: GSM18403 GSM18404 ... GSM18417 (15 total)
## varLabels: title geo_accession ... data_row_count (26 total)
## varMetadata: labelDescription
## featureData
## featureNames: A28102_at AB000114_at ... Z97074_at (7129 total)
## fvarLabels: ID GB_ACC ... Gene Ontology Molecular Function (16 total)
## fvarMetadata: Column Description labelDescription
## experimentData: use 'experimentData(object)'
## pubMedIds: 15284076
## Annotation: GPL80## Colnames of GEOquery object:## [1] "GSM18403" "GSM18404" "GSM18405" "GSM18406" "GSM18407"## Colnames of calculated pre-processed data:## [1] "GSM18403.CEL.gz" "GSM18404.CEL.gz" "GSM18405.CEL.gz" "GSM18406.CEL.gz"
## [5] "GSM18407.CEL.gz"Time for this code chunk: 4.41413021087646
Select column with COPD description
Each experiment has its own annotation and we needed to look for a column describing which sample is a “Control” and which one is “COPD”.
head(pData(geo1))## title geo_accession status submission_date
## GSM18403 01_NML GSM18403 Public on Jun 01 2004 Mar 09 2004
## GSM18404 02_NML GSM18404 Public on Jun 01 2004 Mar 09 2004
## GSM18405 03_NML GSM18405 Public on Jun 01 2004 Mar 09 2004
## GSM18406 04_NML GSM18406 Public on Jun 01 2004 Mar 09 2004
## GSM18407 05_NML GSM18407 Public on Jun 01 2004 Mar 09 2004
## GSM18408 02_ADL GSM18408 Public on Jun 01 2004 Mar 09 2004
## last_update_date type channel_count source_name_ch1 organism_ch1
## GSM18403 Nov 29 2006 RNA 1 lung tissue Homo sapiens
## GSM18404 Nov 29 2006 RNA 1 lung tissue Homo sapiens
## GSM18405 Nov 29 2006 RNA 1 lung tissue Homo sapiens
## GSM18406 Nov 29 2006 RNA 1 lung tissue Homo sapiens
## GSM18407 Nov 29 2006 RNA 1 lung tissue Homo sapiens
## GSM18408 Nov 29 2006 RNA 1 lung tissue Homo sapiens
## molecule_ch1 taxid_ch1
## GSM18403 total RNA 9606
## GSM18404 total RNA 9606
## GSM18405 total RNA 9606
## GSM18406 total RNA 9606
## GSM18407 total RNA 9606
## GSM18408 total RNA 9606
## description platform_id
## GSM18403 Normal lung GPL80
## GSM18404 Normal lung GPL80
## GSM18405 Normal lung GPL80
## GSM18406 Normal lung GPL80
## GSM18407 Normal lung GPL80
## GSM18408 Alpha-1 Antitrypsin Deficiency-related emphysemic lung GPL80
## contact_name contact_email contact_phone
## GSM18403 Christopher,D,Coldren Chris.Coldren@ucdenver.edu 303 724 6056
## GSM18404 Christopher,D,Coldren Chris.Coldren@ucdenver.edu 303 724 6056
## GSM18405 Christopher,D,Coldren Chris.Coldren@ucdenver.edu 303 724 6056
## GSM18406 Christopher,D,Coldren Chris.Coldren@ucdenver.edu 303 724 6056
## GSM18407 Christopher,D,Coldren Chris.Coldren@ucdenver.edu 303 724 6056
## GSM18408 Christopher,D,Coldren Chris.Coldren@ucdenver.edu 303 724 6056
## contact_laboratory contact_department
## GSM18403 Pulmonary Sciences and Critical Care Medicine Medicine
## GSM18404 Pulmonary Sciences and Critical Care Medicine Medicine
## GSM18405 Pulmonary Sciences and Critical Care Medicine Medicine
## GSM18406 Pulmonary Sciences and Critical Care Medicine Medicine
## GSM18407 Pulmonary Sciences and Critical Care Medicine Medicine
## GSM18408 Pulmonary Sciences and Critical Care Medicine Medicine
## contact_institute contact_address
## GSM18403 University of Colorado School of Medicine 12700 East 17th Place
## GSM18404 University of Colorado School of Medicine 12700 East 17th Place
## GSM18405 University of Colorado School of Medicine 12700 East 17th Place
## GSM18406 University of Colorado School of Medicine 12700 East 17th Place
## GSM18407 University of Colorado School of Medicine 12700 East 17th Place
## GSM18408 University of Colorado School of Medicine 12700 East 17th Place
## contact_city contact_state contact_zip/postal_code contact_country
## GSM18403 Aurora CO 80045 USA
## GSM18404 Aurora CO 80045 USA
## GSM18405 Aurora CO 80045 USA
## GSM18406 Aurora CO 80045 USA
## GSM18407 Aurora CO 80045 USA
## GSM18408 Aurora CO 80045 USA
## supplementary_file
## GSM18403 ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM18nnn/GSM18403/suppl/GSM18403.CEL.gz
## GSM18404 ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM18nnn/GSM18404/suppl/GSM18404.CEL.gz
## GSM18405 ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM18nnn/GSM18405/suppl/GSM18405.CEL.gz
## GSM18406 ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM18nnn/GSM18406/suppl/GSM18406.CEL.gz
## GSM18407 ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM18nnn/GSM18407/suppl/GSM18407.CEL.gz
## GSM18408 ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM18nnn/GSM18408/suppl/GSM18408.CEL.gz
## data_row_count
## GSM18403 7129
## GSM18404 7129
## GSM18405 7129
## GSM18406 7129
## GSM18407 7129
## GSM18408 7129Time for this code chunk: 0.0097203254699707
Names will be different but it is important to check that “Control” group is the first level. If need it re-level groups.
pData(geo1)["Disease"] <- factor(pData(geo1)[,"description"],levels = c("Normal lung","'usual' emphysemic lung","Alpha-1 Antitrypsin Deficiency-related emphysemic lung"))
table(pData(geo1)$Disease)##
## Normal lung
## 5
## 'usual' emphysemic lung
## 5
## Alpha-1 Antitrypsin Deficiency-related emphysemic lung
## 5Time for this code chunk: 0.0104248523712158
Differential expression analysis
Using DE() function (described above), we performed a lineal regression model to calculate the logarithm fold change of all genes between a “Control” and a “COPD” group. We also rename colnames adding the GSE ID at the end and finally, we save the output in a .CSV file.
de1 <- DE(geo1)## [1] "(Intercept)"
## [2] "Disease'usual' emphysemic lung"
## [3] "DiseaseAlpha-1 Antitrypsin Deficiency-related emphysemic lung"
colnames(de1) <- str_c(colnames(de1),"_",gse1)
colnames(de1)## [1] "rownames_GSE1122"
## [2] "ID_GSE1122"
## [3] "GB_ACC_GSE1122"
## [4] "SPOT_ID_GSE1122"
## [5] "Species.Scientific.Name_GSE1122"
## [6] "Annotation.Date_GSE1122"
## [7] "Sequence.Type_GSE1122"
## [8] "Sequence.Source_GSE1122"
## [9] "Target.Description_GSE1122"
## [10] "Representative.Public.ID_GSE1122"
## [11] "Gene.Title_GSE1122"
## [12] "Gene.Symbol_GSE1122"
## [13] "ENTREZ_GENE_ID_GSE1122"
## [14] "RefSeq.Transcript.ID_GSE1122"
## [15] "Gene.Ontology.Biological.Process_GSE1122"
## [16] "Gene.Ontology.Cellular.Component_GSE1122"
## [17] "Gene.Ontology.Molecular.Function_GSE1122"
## [18] "logFC_GSE1122"
## [19] "CI.L_GSE1122"
## [20] "CI.R_GSE1122"
## [21] "AveExpr_GSE1122"
## [22] "t_GSE1122"
## [23] "P.Value_GSE1122"
## [24] "adj.P.Val_GSE1122"
## [25] "B_GSE1122"write_csv(de1,
path=str_c(OUTPUT_DIR,"/TableGenes_",gse1,"_",TODAY,".csv")
)Time for this code chunk: 0.682673931121826
GSE1650
Emphysema gene expression was measured using sever, mildly and non emphysema lung tissue. The authors separate samples in two groups, 18 sever and 12 mildly/non emphysema because the limited sample size (7 mildly, 5 non emphysema). From control group, 9 tissues were obtained from smokers with nodules suspicious for lung cancer.
The results of this study shows oxidative stress, extracellular matrix synthesis, and inflammation pathways overexpressed in severe emphysema, whereas endothelium-related was decreased.
The authors didn’t annotate samples individually, the meta information is not available. We assumed that N refers to controls and L to sever emphysema, unfortunately, we can’t separate mildly and non emphysema because of the lack of infomation.
Raw data is in cel files, the platform used is [HG-U133A] Affymetrix. The study was perfomed in Boston, USA (Boston University Medical Center).
Pre-process raw data
We pre-processed raw data using the function rawCEL_normCEL, plots will be shown as additional output.
gse2<- rownames(gse_table)[2]
norm2 <- rawCEL_normCEL(gse2)## Loading required package: pd.hg.u133a## Platform design info loaded.## Reading in : /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE1650/GSM28357.CEL.gz
## Reading in : /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE1650/GSM28358.CEL.gz
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## Background correcting
## Normalizing
## Calculating Expression
norm2## ExpressionSet (storageMode: lockedEnvironment)
## assayData: 22283 features, 30 samples
## element names: exprs
## protocolData
## rowNames: GSM28357.CEL.gz GSM28358.CEL.gz ... GSM28386.CEL.gz (30
## total)
## varLabels: exprs dates
## varMetadata: labelDescription channel
## phenoData
## rowNames: GSM28357.CEL.gz GSM28358.CEL.gz ... GSM28386.CEL.gz (30
## total)
## varLabels: index
## varMetadata: labelDescription channel
## featureData: none
## experimentData: use 'experimentData(object)'
## Annotation: pd.hg.u133aTime for this code chunk: 22.4711818695068
Get annotation
We used GEOquery package to obtain sample annotations and our previous calculated pre-processed values to create an ExpressionSet object.
# get annotation using GEOquery package
geo2 <- get_GEO(gse2,norm2)## Found 1 file(s)## GSE1650_series_matrix.txt.gz## Parsed with column specification:
## cols(
## .default = col_double(),
## ID_REF = col_character()
## )## See spec(...) for full column specifications.## File stored at:## /tmp/Rtmp4ZNwux/GPL96.soft## Data downloaded from GEOquery:## $GSE1650_series_matrix.txt.gz
## ExpressionSet (storageMode: lockedEnvironment)
## assayData: 22283 features, 30 samples
## element names: exprs
## protocolData: none
## phenoData
## sampleNames: GSM28357 GSM28358 ... GSM28386 (30 total)
## varLabels: title geo_accession ... relation (30 total)
## varMetadata: labelDescription
## featureData
## featureNames: 1007_s_at 1053_at ... AFFX-TrpnX-M_at (22283 total)
## fvarLabels: ID GB_ACC ... Gene Ontology Molecular Function (16 total)
## fvarMetadata: Column Description labelDescription
## experimentData: use 'experimentData(object)'
## pubMedIds: 15374838
## Annotation: GPL96## Colnames of GEOquery object:## [1] "GSM28357" "GSM28358" "GSM28359" "GSM28360" "GSM28361"## Colnames of calculated pre-processed data:## [1] "GSM28357.CEL.gz" "GSM28358.CEL.gz" "GSM28359.CEL.gz" "GSM28360.CEL.gz"
## [5] "GSM28361.CEL.gz"Time for this code chunk: 9.52107810974121
Select column with COPD description
Each experiment has its own annotation and we need to look for a column describing which sample is a “Control” and which one is “COPD”.
head(pData(geo2))## title geo_accession status submission_date
## GSM28357 10L GSM28357 Public on Aug 08 2004 Aug 06 2004
## GSM28358 10N GSM28358 Public on Aug 08 2004 Aug 06 2004
## GSM28359 11L GSM28359 Public on Aug 08 2004 Aug 06 2004
## GSM28360 11N GSM28360 Public on Aug 08 2004 Aug 06 2004
## GSM28361 12L GSM28361 Public on Aug 08 2004 Aug 06 2004
## GSM28362 12N GSM28362 Public on Aug 08 2004 Aug 06 2004
## last_update_date type channel_count source_name_ch1 organism_ch1
## GSM28357 Aug 18 2014 RNA 1 Lung Tissue Homo sapiens
## GSM28358 Aug 18 2014 RNA 1 Lung Tissue Homo sapiens
## GSM28359 Aug 18 2014 RNA 1 Lung Tissue Homo sapiens
## GSM28360 Aug 18 2014 RNA 1 Lung Tissue Homo sapiens
## GSM28361 Aug 18 2014 RNA 1 Lung Tissue Homo sapiens
## GSM28362 Aug 18 2014 RNA 1 Lung Tissue Homo sapiens
## molecule_ch1 taxid_ch1
## GSM28357 total RNA 9606
## GSM28358 total RNA 9606
## GSM28359 total RNA 9606
## GSM28360 total RNA 9606
## GSM28361 total RNA 9606
## GSM28362 total RNA 9606
## description
## GSM28357 Lung tissue and ressected lung taken from smokers
## GSM28358 Lung tissue and ressected lung taken from smokers
## GSM28359 Lung tissue and ressected lung taken from smokers
## GSM28360 Lung tissue and ressected lung taken from smokers
## GSM28361 Lung tissue and ressected lung taken from smokers
## GSM28362 Lung tissue and ressected lung taken from smokers
## description.1
## GSM28357 Keywords = Smoking, COPD, lung reduction, airway, molecular screen for spatially restricted transcripts
## GSM28358 Keywords = Smoking, COPD, lung reduction, airway, molecular screen for spatially restricted transcripts
## GSM28359 Keywords = Smoking, COPD, lung reduction, airway, molecular screen for spatially restricted transcripts
## GSM28360 Keywords = Smoking, COPD, lung reduction, airway, molecular screen for spatially restricted transcripts
## GSM28361 Keywords = Smoking, COPD, lung reduction, airway, molecular screen for spatially restricted transcripts
## GSM28362 Keywords = Smoking, COPD, lung reduction, airway, molecular screen for spatially restricted transcripts
## platform_id contact_name contact_email contact_phone contact_fax
## GSM28357 GPL96 Avrum,,Spira aspira@bu.edu 617-638-4860 617-536-8093
## GSM28358 GPL96 Avrum,,Spira aspira@bu.edu 617-638-4860 617-536-8093
## GSM28359 GPL96 Avrum,,Spira aspira@bu.edu 617-638-4860 617-536-8093
## GSM28360 GPL96 Avrum,,Spira aspira@bu.edu 617-638-4860 617-536-8093
## GSM28361 GPL96 Avrum,,Spira aspira@bu.edu 617-638-4860 617-536-8093
## GSM28362 GPL96 Avrum,,Spira aspira@bu.edu 617-638-4860 617-536-8093
## contact_laboratory contact_department
## GSM28357 Pulmonomics Lab Pulmonary and Critical Care Medicine
## GSM28358 Pulmonomics Lab Pulmonary and Critical Care Medicine
## GSM28359 Pulmonomics Lab Pulmonary and Critical Care Medicine
## GSM28360 Pulmonomics Lab Pulmonary and Critical Care Medicine
## GSM28361 Pulmonomics Lab Pulmonary and Critical Care Medicine
## GSM28362 Pulmonomics Lab Pulmonary and Critical Care Medicine
## contact_institute contact_address contact_city
## GSM28357 Boston University Medical Center 715 Albany Street, R304 Boston
## GSM28358 Boston University Medical Center 715 Albany Street, R304 Boston
## GSM28359 Boston University Medical Center 715 Albany Street, R304 Boston
## GSM28360 Boston University Medical Center 715 Albany Street, R304 Boston
## GSM28361 Boston University Medical Center 715 Albany Street, R304 Boston
## GSM28362 Boston University Medical Center 715 Albany Street, R304 Boston
## contact_state contact_zip/postal_code contact_country
## GSM28357 MA 02118 USA
## GSM28358 MA 02118 USA
## GSM28359 MA 02118 USA
## GSM28360 MA 02118 USA
## GSM28361 MA 02118 USA
## GSM28362 MA 02118 USA
## contact_web_link
## GSM28357 http://www.pulmonomics.com
## GSM28358 http://www.pulmonomics.com
## GSM28359 http://www.pulmonomics.com
## GSM28360 http://www.pulmonomics.com
## GSM28361 http://www.pulmonomics.com
## GSM28362 http://www.pulmonomics.com
## supplementary_file
## GSM28357 ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM28nnn/GSM28357/suppl/GSM28357.CEL.gz
## GSM28358 ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM28nnn/GSM28358/suppl/GSM28358.CEL.gz
## GSM28359 ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM28nnn/GSM28359/suppl/GSM28359.CEL.gz
## GSM28360 ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM28nnn/GSM28360/suppl/GSM28360.CEL.gz
## GSM28361 ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM28nnn/GSM28361/suppl/GSM28361.CEL.gz
## GSM28362 ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM28nnn/GSM28362/suppl/GSM28362.CEL.gz
## data_row_count relation
## GSM28357 22283 Reanalyzed by: GSE60486
## GSM28358 22283 Reanalyzed by: GSE60486
## GSM28359 22283 Reanalyzed by: GSE60486
## GSM28360 22283 Reanalyzed by: GSE60486
## GSM28361 22283 Reanalyzed by: GSE60486
## GSM28362 22283 Reanalyzed by: GSE60486Time for this code chunk: 0.0101165771484375
Names will be different but it is important to check that “Control” group is the first level. If need it re-level groups.
pData(geo2)["Disease"] <- str_count(as.character(pData(geo2)[,"title"]),"L")
table(pData(geo2)$Disease)##
## 0 1
## 12 18Time for this code chunk: 0.0110573768615723
Differential expression analysis
Using DE() function (described above), we performed a lineal regression model to calculate the logarithm fold change of all genes between a “Control” and a “COPD” group. We also rename colnames adding the GSE ID at the end and finally, we save the output in a .CSV file.
de2 <- DE(geo2)## [1] "(Intercept)" "Disease"
colnames(de2) <- str_c(colnames(de2),"_",gse2)
colnames(de2)## [1] "rownames_GSE1650"
## [2] "ID_GSE1650"
## [3] "GB_ACC_GSE1650"
## [4] "SPOT_ID_GSE1650"
## [5] "Species.Scientific.Name_GSE1650"
## [6] "Annotation.Date_GSE1650"
## [7] "Sequence.Type_GSE1650"
## [8] "Sequence.Source_GSE1650"
## [9] "Target.Description_GSE1650"
## [10] "Representative.Public.ID_GSE1650"
## [11] "Gene.Title_GSE1650"
## [12] "Gene.Symbol_GSE1650"
## [13] "ENTREZ_GENE_ID_GSE1650"
## [14] "RefSeq.Transcript.ID_GSE1650"
## [15] "Gene.Ontology.Biological.Process_GSE1650"
## [16] "Gene.Ontology.Cellular.Component_GSE1650"
## [17] "Gene.Ontology.Molecular.Function_GSE1650"
## [18] "logFC_GSE1650"
## [19] "CI.L_GSE1650"
## [20] "CI.R_GSE1650"
## [21] "AveExpr_GSE1650"
## [22] "t_GSE1650"
## [23] "P.Value_GSE1650"
## [24] "adj.P.Val_GSE1650"
## [25] "B_GSE1650"write_csv(de2,
path=str_c(OUTPUT_DIR,"/TableGenes_",gse2,"_",TODAY,".csv")
)Time for this code chunk: 1.76264119148254
GSE27597
This experiment measured 8 lung tissue samples from 8 different regions, in total they had 64 gene expression samples, 6 sever COPD patients and 2 donors. The control samples were lung donation organs without suitable recipient, one was a smoker and the other one never smoked.
Results showed inflammation over expressed and tissue repair under expressed in emphysema.
In this GSE id, we can find two experiments that uses different platforms, one measured COPD patients and controls using [HuEx-1_0-st] Affymetrix. And the other one measured fibroblast cell line with [HuGene10stv1_Hs_ENSG] Affymetrix. Authors used Human lung fibroblast cultures (HFL-1) with two concentrations of GHK or with TGFβ1.
Pre-process raw data
We pre-processed raw data using the function rawCEL_normCEL, plots will be shown as additional output.
gse3<- rownames(gse_table)[3]
norm3 <- rawCEL_normCEL(gse3)## Loading required package: pd.huex.1.0.st.v2## Platform design info loaded.## Reading in : /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE27597/GSM684089.CEL.gz
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## Background correcting
## Normalizing
## Calculating Expression
norm3## ExpressionSet (storageMode: lockedEnvironment)
## assayData: 22011 features, 64 samples
## element names: exprs
## protocolData
## rowNames: GSM684089.CEL.gz GSM684090.CEL.gz ... GSM684170.CEL.gz (64
## total)
## varLabels: exprs dates
## varMetadata: labelDescription channel
## phenoData
## rowNames: GSM684089.CEL.gz GSM684090.CEL.gz ... GSM684170.CEL.gz (64
## total)
## varLabels: index
## varMetadata: labelDescription channel
## featureData: none
## experimentData: use 'experimentData(object)'
## Annotation: pd.huex.1.0.st.v2Time for this code chunk: 2.09101250569026
Get annotation
We used GEOquery package to obtain sample annotations and our previous calculated pre-processed values to create an ExpressionSet object.
# get annotation using GEOquery package
geo3 <- get_GEO(gse3,i=2,norm3)## Found 2 file(s)## GSE27597-GPL13243_series_matrix.txt.gz## Parsed with column specification:
## cols(
## ID_REF = col_character(),
## GSM684494 = col_double(),
## GSM684495 = col_double(),
## GSM684496 = col_double(),
## GSM684497 = col_double(),
## GSM684498 = col_double(),
## GSM684499 = col_double(),
## GSM684500 = col_double(),
## GSM684501 = col_double()
## )## File stored at:## /tmp/Rtmp4ZNwux/GPL13243.soft## GSE27597-GPL5175_series_matrix.txt.gz## Parsed with column specification:
## cols(
## .default = col_double()
## )## See spec(...) for full column specifications.## File stored at:## /tmp/Rtmp4ZNwux/GPL5175.soft## Data downloaded from GEOquery:## $`GSE27597-GPL13243_series_matrix.txt.gz`
## ExpressionSet (storageMode: lockedEnvironment)
## assayData: 19793 features, 8 samples
## element names: exprs
## protocolData: none
## phenoData
## sampleNames: GSM684494 GSM684495 ... GSM684501 (8 total)
## varLabels: title geo_accession ... time:ch1 (36 total)
## varMetadata: labelDescription
## featureData
## featureNames: 10000_at 10001_at ... 9_at (19793 total)
## fvarLabels: ID SPOT_ID Description
## fvarMetadata: Column Description labelDescription
## experimentData: use 'experimentData(object)'
## pubMedIds: 22937864
## 24380442
## Annotation: GPL13243
##
## $`GSE27597-GPL5175_series_matrix.txt.gz`
## ExpressionSet (storageMode: lockedEnvironment)
## assayData: 22011 features, 64 samples
## element names: exprs
## protocolData: none
## phenoData
## sampleNames: GSM684089 GSM684090 ... GSM684170 (64 total)
## varLabels: title geo_accession ... slice:ch1 (46 total)
## varMetadata: labelDescription
## featureData
## featureNames: 2315554 2315633 ... 7385696 (22011 total)
## fvarLabels: ID GB_LIST ... category (12 total)
## fvarMetadata: Column Description labelDescription
## experimentData: use 'experimentData(object)'
## pubMedIds: 22937864
## 24380442
## Annotation: GPL5175## Colnames of GEOquery object:## [1] "GSM684089" "GSM684090" "GSM684091" "GSM684092" "GSM684093"## Colnames of calculated pre-processed data:## [1] "GSM684089.CEL.gz" "GSM684090.CEL.gz" "GSM684091.CEL.gz" "GSM684092.CEL.gz"
## [5] "GSM684093.CEL.gz"Time for this code chunk: 18.4546620845795
Select column with COPD description
Each experiment has its own annotation and we needed to look for a column describing which sample is a “Control” and which one is “COPD”.
head(pData(geo3))## title geo_accession status submission_date
## GSM684089 6965-03-COPD GSM684089 Public on Sep 11 2012 Mar 01 2011
## GSM684090 6965-04-COPD GSM684090 Public on Sep 11 2012 Mar 01 2011
## GSM684091 6965-05-COPD GSM684091 Public on Sep 11 2012 Mar 01 2011
## GSM684092 6965-06-COPD GSM684092 Public on Sep 11 2012 Mar 01 2011
## GSM684093 6965-07-COPD GSM684093 Public on Sep 11 2012 Mar 01 2011
## GSM684094 6965-08-COPD GSM684094 Public on Sep 11 2012 Mar 01 2011
## last_update_date type channel_count source_name_ch1 organism_ch1
## GSM684089 Sep 11 2012 RNA 1 Whole lung tissue Homo sapiens
## GSM684090 Sep 11 2012 RNA 1 Whole lung tissue Homo sapiens
## GSM684091 Sep 11 2012 RNA 1 Whole lung tissue Homo sapiens
## GSM684092 Sep 11 2012 RNA 1 Whole lung tissue Homo sapiens
## GSM684093 Sep 11 2012 RNA 1 Whole lung tissue Homo sapiens
## GSM684094 Sep 11 2012 RNA 1 Whole lung tissue Homo sapiens
## characteristics_ch1 characteristics_ch1.1 characteristics_ch1.2
## GSM684089 lm: 870.7318499 patient: 6965 slice: 3
## GSM684090 lm: 639.6153396 patient: 6965 slice: 4
## GSM684091 lm: 982.9172824 patient: 6965 slice: 5
## GSM684092 lm: 801.976686 patient: 6965 slice: 6
## GSM684093 lm: 726.5157591 patient: 6965 slice: 7
## GSM684094 lm: 663.336977 patient: 6965 slice: 8
## characteristics_ch1.3 characteristics_ch1.4 characteristics_ch1.5
## GSM684089 copd status: 1 Sex: Male age: 62
## GSM684090 copd status: 1 Sex: Male age: 62
## GSM684091 copd status: 1 Sex: Male age: 62
## GSM684092 copd status: 1 Sex: Male age: 62
## GSM684093 copd status: 1 Sex: Male age: 62
## GSM684094 copd status: 1 Sex: Male age: 62
## characteristics_ch1.6 characteristics_ch1.7 molecule_ch1
## GSM684089 pack years: 50 notes: none total RNA
## GSM684090 pack years: 50 notes: none total RNA
## GSM684091 pack years: 50 notes: none total RNA
## GSM684092 pack years: 50 notes: none total RNA
## GSM684093 pack years: 50 notes: none total RNA
## GSM684094 pack years: 50 notes: none total RNA
## extract_protocol_ch1
## GSM684089 High molecular weight RNA was isolated from tissue cores using the miRNeasy Mini Kit (Qiagen). The RNA integrity was assessed using an Agilent 2100 Bioanalyzer and RNA purity was assessed using a NanoDrop spectrophotometer. One ug of RNA was processed and used as starting material for the microarray studies.
## GSM684090 High molecular weight RNA was isolated from tissue cores using the miRNeasy Mini Kit (Qiagen). The RNA integrity was assessed using an Agilent 2100 Bioanalyzer and RNA purity was assessed using a NanoDrop spectrophotometer. One ug of RNA was processed and used as starting material for the microarray studies.
## GSM684091 High molecular weight RNA was isolated from tissue cores using the miRNeasy Mini Kit (Qiagen). The RNA integrity was assessed using an Agilent 2100 Bioanalyzer and RNA purity was assessed using a NanoDrop spectrophotometer. One ug of RNA was processed and used as starting material for the microarray studies.
## GSM684092 High molecular weight RNA was isolated from tissue cores using the miRNeasy Mini Kit (Qiagen). The RNA integrity was assessed using an Agilent 2100 Bioanalyzer and RNA purity was assessed using a NanoDrop spectrophotometer. One ug of RNA was processed and used as starting material for the microarray studies.
## GSM684093 High molecular weight RNA was isolated from tissue cores using the miRNeasy Mini Kit (Qiagen). The RNA integrity was assessed using an Agilent 2100 Bioanalyzer and RNA purity was assessed using a NanoDrop spectrophotometer. One ug of RNA was processed and used as starting material for the microarray studies.
## GSM684094 High molecular weight RNA was isolated from tissue cores using the miRNeasy Mini Kit (Qiagen). The RNA integrity was assessed using an Agilent 2100 Bioanalyzer and RNA purity was assessed using a NanoDrop spectrophotometer. One ug of RNA was processed and used as starting material for the microarray studies.
## label_ch1
## GSM684089 biotin
## GSM684090 biotin
## GSM684091 biotin
## GSM684092 biotin
## GSM684093 biotin
## GSM684094 biotin
## label_protocol_ch1
## GSM684089 Ribosomal RNA was first removed using the RiboMinus Human/Mouse Transcriptome Isolation Kit (Invitrogen, Carlsbad, CA). This treated RNA was then converted to cDNA and subsequently processed and biotin-labeled.
## GSM684090 Ribosomal RNA was first removed using the RiboMinus Human/Mouse Transcriptome Isolation Kit (Invitrogen, Carlsbad, CA). This treated RNA was then converted to cDNA and subsequently processed and biotin-labeled.
## GSM684091 Ribosomal RNA was first removed using the RiboMinus Human/Mouse Transcriptome Isolation Kit (Invitrogen, Carlsbad, CA). This treated RNA was then converted to cDNA and subsequently processed and biotin-labeled.
## GSM684092 Ribosomal RNA was first removed using the RiboMinus Human/Mouse Transcriptome Isolation Kit (Invitrogen, Carlsbad, CA). This treated RNA was then converted to cDNA and subsequently processed and biotin-labeled.
## GSM684093 Ribosomal RNA was first removed using the RiboMinus Human/Mouse Transcriptome Isolation Kit (Invitrogen, Carlsbad, CA). This treated RNA was then converted to cDNA and subsequently processed and biotin-labeled.
## GSM684094 Ribosomal RNA was first removed using the RiboMinus Human/Mouse Transcriptome Isolation Kit (Invitrogen, Carlsbad, CA). This treated RNA was then converted to cDNA and subsequently processed and biotin-labeled.
## taxid_ch1
## GSM684089 9606
## GSM684090 9606
## GSM684091 9606
## GSM684092 9606
## GSM684093 9606
## GSM684094 9606
## hyb_protocol
## GSM684089 cDNA was end-labeled with a biotinylated dideoxynucleotide using terminal transferase. Five and a half micrograms of biotinylated cDNA was added to a hybridization cocktail, loaded on a Human Exon 1.0 ST GeneChip and hybridized for 16 hours at 45 ºC and 60 rpm. Following hybridization, the array was washed and stained according to the standard Affymetrix protocol.
## GSM684090 cDNA was end-labeled with a biotinylated dideoxynucleotide using terminal transferase. Five and a half micrograms of biotinylated cDNA was added to a hybridization cocktail, loaded on a Human Exon 1.0 ST GeneChip and hybridized for 16 hours at 45 ºC and 60 rpm. Following hybridization, the array was washed and stained according to the standard Affymetrix protocol.
## GSM684091 cDNA was end-labeled with a biotinylated dideoxynucleotide using terminal transferase. Five and a half micrograms of biotinylated cDNA was added to a hybridization cocktail, loaded on a Human Exon 1.0 ST GeneChip and hybridized for 16 hours at 45 ºC and 60 rpm. Following hybridization, the array was washed and stained according to the standard Affymetrix protocol.
## GSM684092 cDNA was end-labeled with a biotinylated dideoxynucleotide using terminal transferase. Five and a half micrograms of biotinylated cDNA was added to a hybridization cocktail, loaded on a Human Exon 1.0 ST GeneChip and hybridized for 16 hours at 45 ºC and 60 rpm. Following hybridization, the array was washed and stained according to the standard Affymetrix protocol.
## GSM684093 cDNA was end-labeled with a biotinylated dideoxynucleotide using terminal transferase. Five and a half micrograms of biotinylated cDNA was added to a hybridization cocktail, loaded on a Human Exon 1.0 ST GeneChip and hybridized for 16 hours at 45 ºC and 60 rpm. Following hybridization, the array was washed and stained according to the standard Affymetrix protocol.
## GSM684094 cDNA was end-labeled with a biotinylated dideoxynucleotide using terminal transferase. Five and a half micrograms of biotinylated cDNA was added to a hybridization cocktail, loaded on a Human Exon 1.0 ST GeneChip and hybridized for 16 hours at 45 ºC and 60 rpm. Following hybridization, the array was washed and stained according to the standard Affymetrix protocol.
## scan_protocol
## GSM684089 Arrays were scanned using an Affymetrix GeneChip Scanner 3000. These scans were used to generate CEL files for each array.
## GSM684090 Arrays were scanned using an Affymetrix GeneChip Scanner 3000. These scans were used to generate CEL files for each array.
## GSM684091 Arrays were scanned using an Affymetrix GeneChip Scanner 3000. These scans were used to generate CEL files for each array.
## GSM684092 Arrays were scanned using an Affymetrix GeneChip Scanner 3000. These scans were used to generate CEL files for each array.
## GSM684093 Arrays were scanned using an Affymetrix GeneChip Scanner 3000. These scans were used to generate CEL files for each array.
## GSM684094 Arrays were scanned using an Affymetrix GeneChip Scanner 3000. These scans were used to generate CEL files for each array.
## description
## GSM684089 RNA was treated for removal of ribosomal RNA, from which cDNA was synthesized, biotin-labeled, and hybridized to Human Exon 1.0 ST GeneChips. Transcript level data was obtained with RMA and the Affymetrix CDF using Expression Console Version 1.0 software.
## GSM684090 RNA was treated for removal of ribosomal RNA, from which cDNA was synthesized, biotin-labeled, and hybridized to Human Exon 1.0 ST GeneChips. Transcript level data was obtained with RMA and the Affymetrix CDF using Expression Console Version 1.0 software.
## GSM684091 RNA was treated for removal of ribosomal RNA, from which cDNA was synthesized, biotin-labeled, and hybridized to Human Exon 1.0 ST GeneChips. Transcript level data was obtained with RMA and the Affymetrix CDF using Expression Console Version 1.0 software.
## GSM684092 RNA was treated for removal of ribosomal RNA, from which cDNA was synthesized, biotin-labeled, and hybridized to Human Exon 1.0 ST GeneChips. Transcript level data was obtained with RMA and the Affymetrix CDF using Expression Console Version 1.0 software.
## GSM684093 RNA was treated for removal of ribosomal RNA, from which cDNA was synthesized, biotin-labeled, and hybridized to Human Exon 1.0 ST GeneChips. Transcript level data was obtained with RMA and the Affymetrix CDF using Expression Console Version 1.0 software.
## GSM684094 RNA was treated for removal of ribosomal RNA, from which cDNA was synthesized, biotin-labeled, and hybridized to Human Exon 1.0 ST GeneChips. Transcript level data was obtained with RMA and the Affymetrix CDF using Expression Console Version 1.0 software.
## data_processing
## GSM684089 Expression Console Version 1.1 (Affymetrix Inc.) was used to generate transcript-level gene expression estimates via the robust multichip average (RMA) algorithm using the Affymetrix CDF (Gene Level - Core: RMA Sketch). Gene symbols of transcript ids were retrieved using DAVID (http://david.abcc.ncifcrf.gov/). No filtering of genes was performed before statistical testing.
## GSM684090 Expression Console Version 1.1 (Affymetrix Inc.) was used to generate transcript-level gene expression estimates via the robust multichip average (RMA) algorithm using the Affymetrix CDF (Gene Level - Core: RMA Sketch). Gene symbols of transcript ids were retrieved using DAVID (http://david.abcc.ncifcrf.gov/). No filtering of genes was performed before statistical testing.
## GSM684091 Expression Console Version 1.1 (Affymetrix Inc.) was used to generate transcript-level gene expression estimates via the robust multichip average (RMA) algorithm using the Affymetrix CDF (Gene Level - Core: RMA Sketch). Gene symbols of transcript ids were retrieved using DAVID (http://david.abcc.ncifcrf.gov/). No filtering of genes was performed before statistical testing.
## GSM684092 Expression Console Version 1.1 (Affymetrix Inc.) was used to generate transcript-level gene expression estimates via the robust multichip average (RMA) algorithm using the Affymetrix CDF (Gene Level - Core: RMA Sketch). Gene symbols of transcript ids were retrieved using DAVID (http://david.abcc.ncifcrf.gov/). No filtering of genes was performed before statistical testing.
## GSM684093 Expression Console Version 1.1 (Affymetrix Inc.) was used to generate transcript-level gene expression estimates via the robust multichip average (RMA) algorithm using the Affymetrix CDF (Gene Level - Core: RMA Sketch). Gene symbols of transcript ids were retrieved using DAVID (http://david.abcc.ncifcrf.gov/). No filtering of genes was performed before statistical testing.
## GSM684094 Expression Console Version 1.1 (Affymetrix Inc.) was used to generate transcript-level gene expression estimates via the robust multichip average (RMA) algorithm using the Affymetrix CDF (Gene Level - Core: RMA Sketch). Gene symbols of transcript ids were retrieved using DAVID (http://david.abcc.ncifcrf.gov/). No filtering of genes was performed before statistical testing.
## data_processing.1
## GSM684089 Quantile sketch normalized transcript level expression using the RMA algorithm with the Affymetrix CDF
## GSM684090 Quantile sketch normalized transcript level expression using the RMA algorithm with the Affymetrix CDF
## GSM684091 Quantile sketch normalized transcript level expression using the RMA algorithm with the Affymetrix CDF
## GSM684092 Quantile sketch normalized transcript level expression using the RMA algorithm with the Affymetrix CDF
## GSM684093 Quantile sketch normalized transcript level expression using the RMA algorithm with the Affymetrix CDF
## GSM684094 Quantile sketch normalized transcript level expression using the RMA algorithm with the Affymetrix CDF
## platform_id contact_name contact_email contact_institute
## GSM684089 GPL5175 Joshua,David,Campbell camp@bu.edu Boston University
## GSM684090 GPL5175 Joshua,David,Campbell camp@bu.edu Boston University
## GSM684091 GPL5175 Joshua,David,Campbell camp@bu.edu Boston University
## GSM684092 GPL5175 Joshua,David,Campbell camp@bu.edu Boston University
## GSM684093 GPL5175 Joshua,David,Campbell camp@bu.edu Boston University
## GSM684094 GPL5175 Joshua,David,Campbell camp@bu.edu Boston University
## contact_address contact_city contact_state
## GSM684089 72 East Concord St., E-632 Boston MA
## GSM684090 72 East Concord St., E-632 Boston MA
## GSM684091 72 East Concord St., E-632 Boston MA
## GSM684092 72 East Concord St., E-632 Boston MA
## GSM684093 72 East Concord St., E-632 Boston MA
## GSM684094 72 East Concord St., E-632 Boston MA
## contact_zip/postal_code contact_country
## GSM684089 02118 USA
## GSM684090 02118 USA
## GSM684091 02118 USA
## GSM684092 02118 USA
## GSM684093 02118 USA
## GSM684094 02118 USA
## supplementary_file
## GSM684089 ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM684nnn/GSM684089/suppl/GSM684089.CEL.gz
## GSM684090 ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM684nnn/GSM684090/suppl/GSM684090.CEL.gz
## GSM684091 ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM684nnn/GSM684091/suppl/GSM684091.CEL.gz
## GSM684092 ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM684nnn/GSM684092/suppl/GSM684092.CEL.gz
## GSM684093 ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM684nnn/GSM684093/suppl/GSM684093.CEL.gz
## GSM684094 ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM684nnn/GSM684094/suppl/GSM684094.CEL.gz
## data_row_count age:ch1 copd status:ch1 lm:ch1 notes:ch1
## GSM684089 22011 62 1 870.7318499 none
## GSM684090 22011 62 1 639.6153396 none
## GSM684091 22011 62 1 982.9172824 none
## GSM684092 22011 62 1 801.976686 none
## GSM684093 22011 62 1 726.5157591 none
## GSM684094 22011 62 1 663.336977 none
## pack years:ch1 patient:ch1 Sex:ch1 slice:ch1
## GSM684089 50 6965 Male 3
## GSM684090 50 6965 Male 4
## GSM684091 50 6965 Male 5
## GSM684092 50 6965 Male 6
## GSM684093 50 6965 Male 7
## GSM684094 50 6965 Male 8Time for this code chunk: 0.0216760635375977
Names will be different but it is important to check that “Control” group is the first level. If need it re-level groups.
pData(geo3)["Disease"] <- pData(geo3)[,"characteristics_ch1.3"]
table(pData(geo3)$Disease)##
## copd status: 0 copd status: 1
## 16 48Time for this code chunk: 0.0159335136413574
Differential expression analysis
Using DE() function (described above), we performed a lineal regression model to calculate the logarithm fold change of all genes between a “Control” and a “COPD” group. We also rename colnames adding the GSE ID at the end and finally, we save the output in a .CSV file.
de3 <- DE(geo3)## [1] "(Intercept)" "Diseasecopd status: 1"
colnames(de3) <- str_c(colnames(de3),"_",gse3)
colnames(de3)## [1] "rownames_GSE27597" "ID_GSE27597"
## [3] "GB_LIST_GSE27597" "SPOT_ID_GSE27597"
## [5] "seqname_GSE27597" "RANGE_GB_GSE27597"
## [7] "RANGE_STRAND_GSE27597" "RANGE_START_GSE27597"
## [9] "RANGE_STOP_GSE27597" "total_probes_GSE27597"
## [11] "gene_assignment_GSE27597" "mrna_assignment_GSE27597"
## [13] "category_GSE27597" "logFC_GSE27597"
## [15] "CI.L_GSE27597" "CI.R_GSE27597"
## [17] "AveExpr_GSE27597" "t_GSE27597"
## [19] "P.Value_GSE27597" "adj.P.Val_GSE27597"
## [21] "B_GSE27597"write_csv(de3,
path=str_c(OUTPUT_DIR,"/TableGenes_",gse3,"_",TODAY,".csv")
)Time for this code chunk: 1.68914937973022
GSE37768
The aim of this experiment was to identify genes deferential regulated between Normal and COPD lungs. They used two control group, one smokers and another one non smokers.
The experiment doesn’t have a related article but these two (https://www.sciencedirect.com/science/article/pii/S1094553910001240?via%3Dihub#fig5 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4022517/) describe a very similar design and have one of the contact contributors from the GEO experiment.
There are 9 non smokers, 11 smokers and 18 COPD lung tissue, but no more information is available or traceable about the samples.
Pre-process raw data
We pre-processed raw data using the function rawCEL_normCEL, plots will be shown as additional output.
gse4<- rownames(gse_table)[4]
norm4 <- rawCEL_normCEL(gse4)## Loading required package: pd.hg.u133.plus.2## Platform design info loaded.## Reading in : /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE37768/GSM927630_NS1-CEL1.CEL.gz
## Reading in : /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE37768/GSM927631_NS2-CEL10.CEL.gz
## Reading in : /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE37768/GSM927632_NS3-CEL11.CEL.gz
## Reading in : /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE37768/GSM927633_NS4-CEL12.CEL.gz
## Reading in : /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE37768/GSM927634_NS5-CEL13.CEL.gz
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## Reading in : /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE37768/GSM927643_S5-CEL22.CEL.gz
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## Reading in : /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE37768/GSM927649_S11-CEL37.CEL.gz
## Reading in : /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE37768/GSM927650_COPD1-CEL2.CEL.gz
## Reading in : /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE37768/GSM927651_COPD2-CEL3.CEL.gz
## Reading in : /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE37768/GSM927652_COPD3-CEL4.CEL.gz
## Reading in : /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE37768/GSM927653_COPD4-CEL5.CEL.gz
## Reading in : /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE37768/GSM927654_COPD5-CEL6.CEL.gz
## Reading in : /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE37768/GSM927655_COPD6-CEL7.CEL.gz
## Reading in : /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE37768/GSM927656_COPD7-CEL8.CEL.gz
## Reading in : /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE37768/GSM927657_COPD8-CEL9.CEL.gz
## Reading in : /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE37768/GSM927658_COPD9-CEL17.CEL.gz
## Reading in : /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE37768/GSM927659_COPD10-CEL18.CEL.gz
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## Reading in : /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE37768/GSM927661_COPD12-CEL24.CEL.gz
## Reading in : /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE37768/GSM927662_COPD13-CEL25.CEL.gz
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## Reading in : /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE37768/GSM927664_COPD15-CEL29.CEL.gz
## Reading in : /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE37768/GSM927665_COPD16-CEL32.CEL.gz
## Reading in : /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE37768/GSM927666_COPD17-CEL33.CEL.gz
## Reading in : /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE37768/GSM927667_COPD18-CEL38.CEL.gz
## Background correcting
## Normalizing
## Calculating Expression
norm4## ExpressionSet (storageMode: lockedEnvironment)
## assayData: 54675 features, 38 samples
## element names: exprs
## protocolData
## rowNames: GSM927630_NS1-CEL1.CEL.gz GSM927631_NS2-CEL10.CEL.gz ...
## GSM927667_COPD18-CEL38.CEL.gz (38 total)
## varLabels: exprs dates
## varMetadata: labelDescription channel
## phenoData
## rowNames: GSM927630_NS1-CEL1.CEL.gz GSM927631_NS2-CEL10.CEL.gz ...
## GSM927667_COPD18-CEL38.CEL.gz (38 total)
## varLabels: index
## varMetadata: labelDescription channel
## featureData: none
## experimentData: use 'experimentData(object)'
## Annotation: pd.hg.u133.plus.2Time for this code chunk: 47.142466545105
Get annotation
We used GEOquery package to obtain sample annotations and our previous calculated pre-processed values to create an ExpressionSet object.
# get annotation using GEOquery package
geo4 <- get_GEO(gse4,norm4)## Found 1 file(s)## GSE37768_series_matrix.txt.gz## Parsed with column specification:
## cols(
## .default = col_double(),
## ID_REF = col_character()
## )## See spec(...) for full column specifications.## File stored at:## /tmp/Rtmp4ZNwux/GPL570.soft## Data downloaded from GEOquery:## $GSE37768_series_matrix.txt.gz
## ExpressionSet (storageMode: lockedEnvironment)
## assayData: 54675 features, 38 samples
## element names: exprs
## protocolData: none
## phenoData
## sampleNames: GSM927630 GSM927631 ... GSM927667 (38 total)
## varLabels: title geo_accession ... tissue:ch1 (33 total)
## varMetadata: labelDescription
## featureData
## featureNames: 1007_s_at 1053_at ... AFFX-TrpnX-M_at (54675 total)
## fvarLabels: ID GB_ACC ... Gene Ontology Molecular Function (16 total)
## fvarMetadata: Column Description labelDescription
## experimentData: use 'experimentData(object)'
## Annotation: GPL570## Colnames of GEOquery object:## [1] "GSM927630" "GSM927631" "GSM927632" "GSM927633" "GSM927634"## Colnames of calculated pre-processed data:## [1] "GSM927630_NS1-CEL1.CEL.gz" "GSM927631_NS2-CEL10.CEL.gz"
## [3] "GSM927632_NS3-CEL11.CEL.gz" "GSM927633_NS4-CEL12.CEL.gz"
## [5] "GSM927634_NS5-CEL13.CEL.gz"Time for this code chunk: 17.9869315624237
Select column with COPD description
Each experiment has its own annotation and we needed to look for a column describing which sample is a “Control” and which one is “COPD”.
head(pData(geo4))## title geo_accession status submission_date
## GSM927630 Lung tissue_NS1 GSM927630 Public on Aug 25 2016 May 04 2012
## GSM927631 Lung tissue_NS2 GSM927631 Public on Aug 25 2016 May 04 2012
## GSM927632 Lung tissue_NS3 GSM927632 Public on Aug 25 2016 May 04 2012
## GSM927633 Lung tissue_NS4 GSM927633 Public on Aug 25 2016 May 04 2012
## GSM927634 Lung tissue_NS5 GSM927634 Public on Aug 25 2016 May 04 2012
## GSM927635 Lung tissue_NS6 GSM927635 Public on Aug 25 2016 May 04 2012
## last_update_date type channel_count
## GSM927630 Aug 25 2016 RNA 1
## GSM927631 Aug 25 2016 RNA 1
## GSM927632 Aug 25 2016 RNA 1
## GSM927633 Aug 25 2016 RNA 1
## GSM927634 Aug 25 2016 RNA 1
## GSM927635 Aug 25 2016 RNA 1
## source_name_ch1 organism_ch1
## GSM927630 Peripheral lung tissue, nonsmoker Homo sapiens
## GSM927631 Peripheral lung tissue, nonsmoker Homo sapiens
## GSM927632 Peripheral lung tissue, nonsmoker Homo sapiens
## GSM927633 Peripheral lung tissue, nonsmoker Homo sapiens
## GSM927634 Peripheral lung tissue, nonsmoker Homo sapiens
## GSM927635 Peripheral lung tissue, nonsmoker Homo sapiens
## characteristics_ch1 characteristics_ch1.1 molecule_ch1
## GSM927630 tissue: Peripheral lung tissue phenotype: Nonsmoker total RNA
## GSM927631 tissue: Peripheral lung tissue phenotype: Nonsmoker total RNA
## GSM927632 tissue: Peripheral lung tissue phenotype: Nonsmoker total RNA
## GSM927633 tissue: Peripheral lung tissue phenotype: Nonsmoker total RNA
## GSM927634 tissue: Peripheral lung tissue phenotype: Nonsmoker total RNA
## GSM927635 tissue: Peripheral lung tissue phenotype: Nonsmoker total RNA
## extract_protocol_ch1
## GSM927630 RNA was prepared from grossly homogenous pieces of tissue (100-200 mg) with an RNeasy Midi kit (Qiagen, Valencia, CA) according to the manufactures instructions
## GSM927631 RNA was prepared from grossly homogenous pieces of tissue (100-200 mg) with an RNeasy Midi kit (Qiagen, Valencia, CA) according to the manufactures instructions
## GSM927632 RNA was prepared from grossly homogenous pieces of tissue (100-200 mg) with an RNeasy Midi kit (Qiagen, Valencia, CA) according to the manufactures instructions
## GSM927633 RNA was prepared from grossly homogenous pieces of tissue (100-200 mg) with an RNeasy Midi kit (Qiagen, Valencia, CA) according to the manufactures instructions
## GSM927634 RNA was prepared from grossly homogenous pieces of tissue (100-200 mg) with an RNeasy Midi kit (Qiagen, Valencia, CA) according to the manufactures instructions
## GSM927635 RNA was prepared from grossly homogenous pieces of tissue (100-200 mg) with an RNeasy Midi kit (Qiagen, Valencia, CA) according to the manufactures instructions
## label_ch1
## GSM927630 biotin
## GSM927631 biotin
## GSM927632 biotin
## GSM927633 biotin
## GSM927634 biotin
## GSM927635 biotin
## label_protocol_ch1
## GSM927630 cRNA amplification and biotin labeling was synthesized from 2ug cDNA by means of an in vitro transcription reaction in the presence of T7 RNA Polymerase and of biotinylated nucleotide analog/ribonucleotide mix (GeneChip IVT labeling Kit, Affymetrix Inc, Santa Clara, CA)
## GSM927631 cRNA amplification and biotin labeling was synthesized from 2ug cDNA by means of an in vitro transcription reaction in the presence of T7 RNA Polymerase and of biotinylated nucleotide analog/ribonucleotide mix (GeneChip IVT labeling Kit, Affymetrix Inc, Santa Clara, CA)
## GSM927632 cRNA amplification and biotin labeling was synthesized from 2ug cDNA by means of an in vitro transcription reaction in the presence of T7 RNA Polymerase and of biotinylated nucleotide analog/ribonucleotide mix (GeneChip IVT labeling Kit, Affymetrix Inc, Santa Clara, CA)
## GSM927633 cRNA amplification and biotin labeling was synthesized from 2ug cDNA by means of an in vitro transcription reaction in the presence of T7 RNA Polymerase and of biotinylated nucleotide analog/ribonucleotide mix (GeneChip IVT labeling Kit, Affymetrix Inc, Santa Clara, CA)
## GSM927634 cRNA amplification and biotin labeling was synthesized from 2ug cDNA by means of an in vitro transcription reaction in the presence of T7 RNA Polymerase and of biotinylated nucleotide analog/ribonucleotide mix (GeneChip IVT labeling Kit, Affymetrix Inc, Santa Clara, CA)
## GSM927635 cRNA amplification and biotin labeling was synthesized from 2ug cDNA by means of an in vitro transcription reaction in the presence of T7 RNA Polymerase and of biotinylated nucleotide analog/ribonucleotide mix (GeneChip IVT labeling Kit, Affymetrix Inc, Santa Clara, CA)
## taxid_ch1
## GSM927630 9606
## GSM927631 9606
## GSM927632 9606
## GSM927633 9606
## GSM927634 9606
## GSM927635 9606
## hyb_protocol
## GSM927630 Biotin-labeled cRNA was cleanup and quantified and subsequently it was fragmented by metal-induced hydrolysis (GeneChip Sample Cleanup Module, Affymetrix Inc). Hybridization was performed with GeneChip® Hybridization Wash and Stain Kit (Affymetrix Inc) according to manufactures instructions.
## GSM927631 Biotin-labeled cRNA was cleanup and quantified and subsequently it was fragmented by metal-induced hydrolysis (GeneChip Sample Cleanup Module, Affymetrix Inc). Hybridization was performed with GeneChip® Hybridization Wash and Stain Kit (Affymetrix Inc) according to manufactures instructions.
## GSM927632 Biotin-labeled cRNA was cleanup and quantified and subsequently it was fragmented by metal-induced hydrolysis (GeneChip Sample Cleanup Module, Affymetrix Inc). Hybridization was performed with GeneChip® Hybridization Wash and Stain Kit (Affymetrix Inc) according to manufactures instructions.
## GSM927633 Biotin-labeled cRNA was cleanup and quantified and subsequently it was fragmented by metal-induced hydrolysis (GeneChip Sample Cleanup Module, Affymetrix Inc). Hybridization was performed with GeneChip® Hybridization Wash and Stain Kit (Affymetrix Inc) according to manufactures instructions.
## GSM927634 Biotin-labeled cRNA was cleanup and quantified and subsequently it was fragmented by metal-induced hydrolysis (GeneChip Sample Cleanup Module, Affymetrix Inc). Hybridization was performed with GeneChip® Hybridization Wash and Stain Kit (Affymetrix Inc) according to manufactures instructions.
## GSM927635 Biotin-labeled cRNA was cleanup and quantified and subsequently it was fragmented by metal-induced hydrolysis (GeneChip Sample Cleanup Module, Affymetrix Inc). Hybridization was performed with GeneChip® Hybridization Wash and Stain Kit (Affymetrix Inc) according to manufactures instructions.
## scan_protocol
## GSM927630 GeneChips were scanned using the GeneChip®Scanner 3000 7G (Affymetrix Inc)
## GSM927631 GeneChips were scanned using the GeneChip®Scanner 3000 7G (Affymetrix Inc)
## GSM927632 GeneChips were scanned using the GeneChip®Scanner 3000 7G (Affymetrix Inc)
## GSM927633 GeneChips were scanned using the GeneChip®Scanner 3000 7G (Affymetrix Inc)
## GSM927634 GeneChips were scanned using the GeneChip®Scanner 3000 7G (Affymetrix Inc)
## GSM927635 GeneChips were scanned using the GeneChip®Scanner 3000 7G (Affymetrix Inc)
## description
## GSM927630 NS-CEL1
## GSM927631 NS-CEL10
## GSM927632 NS-CEL11
## GSM927633 NS-CEL12
## GSM927634 NS-CEL13
## GSM927635 NS-CEL20
## data_processing
## GSM927630 Expression measures were normalized and summarized using the GC content adjusted-Robust Multiarray Analysis (GC-RMA) methodology
## GSM927631 Expression measures were normalized and summarized using the GC content adjusted-Robust Multiarray Analysis (GC-RMA) methodology
## GSM927632 Expression measures were normalized and summarized using the GC content adjusted-Robust Multiarray Analysis (GC-RMA) methodology
## GSM927633 Expression measures were normalized and summarized using the GC content adjusted-Robust Multiarray Analysis (GC-RMA) methodology
## GSM927634 Expression measures were normalized and summarized using the GC content adjusted-Robust Multiarray Analysis (GC-RMA) methodology
## GSM927635 Expression measures were normalized and summarized using the GC content adjusted-Robust Multiarray Analysis (GC-RMA) methodology
## platform_id contact_name contact_email
## GSM927630 GPL570 Ricardo,,Bastos rbastos@clinic.ub.es
## GSM927631 GPL570 Ricardo,,Bastos rbastos@clinic.ub.es
## GSM927632 GPL570 Ricardo,,Bastos rbastos@clinic.ub.es
## GSM927633 GPL570 Ricardo,,Bastos rbastos@clinic.ub.es
## GSM927634 GPL570 Ricardo,,Bastos rbastos@clinic.ub.es
## GSM927635 GPL570 Ricardo,,Bastos rbastos@clinic.ub.es
## contact_department
## GSM927630 Cell Biology, Neurosciences and Immunology
## GSM927631 Cell Biology, Neurosciences and Immunology
## GSM927632 Cell Biology, Neurosciences and Immunology
## GSM927633 Cell Biology, Neurosciences and Immunology
## GSM927634 Cell Biology, Neurosciences and Immunology
## GSM927635 Cell Biology, Neurosciences and Immunology
## contact_institute contact_address contact_city
## GSM927630 IDIBAPS/University of Barcelona Rosellón 149-153 Barcelona
## GSM927631 IDIBAPS/University of Barcelona Rosellón 149-153 Barcelona
## GSM927632 IDIBAPS/University of Barcelona Rosellón 149-153 Barcelona
## GSM927633 IDIBAPS/University of Barcelona Rosellón 149-153 Barcelona
## GSM927634 IDIBAPS/University of Barcelona Rosellón 149-153 Barcelona
## GSM927635 IDIBAPS/University of Barcelona Rosellón 149-153 Barcelona
## contact_zip/postal_code contact_country
## GSM927630 08036 Spain
## GSM927631 08036 Spain
## GSM927632 08036 Spain
## GSM927633 08036 Spain
## GSM927634 08036 Spain
## GSM927635 08036 Spain
## supplementary_file
## GSM927630 ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM927nnn/GSM927630/suppl/GSM927630_NS1-CEL1.CEL.gz
## GSM927631 ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM927nnn/GSM927631/suppl/GSM927631_NS2-CEL10.CEL.gz
## GSM927632 ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM927nnn/GSM927632/suppl/GSM927632_NS3-CEL11.CEL.gz
## GSM927633 ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM927nnn/GSM927633/suppl/GSM927633_NS4-CEL12.CEL.gz
## GSM927634 ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM927nnn/GSM927634/suppl/GSM927634_NS5-CEL13.CEL.gz
## GSM927635 ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM927nnn/GSM927635/suppl/GSM927635_NS6-CEL20.CEL.gz
## data_row_count phenotype:ch1 tissue:ch1
## GSM927630 54675 Nonsmoker Peripheral lung tissue
## GSM927631 54675 Nonsmoker Peripheral lung tissue
## GSM927632 54675 Nonsmoker Peripheral lung tissue
## GSM927633 54675 Nonsmoker Peripheral lung tissue
## GSM927634 54675 Nonsmoker Peripheral lung tissue
## GSM927635 54675 Nonsmoker Peripheral lung tissueTime for this code chunk: 0.0166659355163574
Names will be different but it is important to check that “Control” group is the first level. If need it re-level groups.
pData(geo4)["Disease"] <- factor(pData(geo4)[,"phenotype:ch1"])
table(pData(geo4)$Disease)##
## healthy smoker moderate COPD Nonsmoker
## 11 18 9Time for this code chunk: 0.0104644298553467
Differential expression analysis
Using DE() function (described above), we performed a lineal regression model to calculate the logarithm fold change of all genes between a “Control” and a “COPD” group. We also rename colnames adding the GSE ID at the end and finally, we save the output in a .CSV file.
de4 <- DE(geo4)## [1] "(Intercept)" "Diseasemoderate COPD" "DiseaseNonsmoker"
colnames(de4) <- str_c(colnames(de4),"_",gse4)
colnames(de4)## [1] "rownames_GSE37768"
## [2] "ID_GSE37768"
## [3] "GB_ACC_GSE37768"
## [4] "SPOT_ID_GSE37768"
## [5] "Species.Scientific.Name_GSE37768"
## [6] "Annotation.Date_GSE37768"
## [7] "Sequence.Type_GSE37768"
## [8] "Sequence.Source_GSE37768"
## [9] "Target.Description_GSE37768"
## [10] "Representative.Public.ID_GSE37768"
## [11] "Gene.Title_GSE37768"
## [12] "Gene.Symbol_GSE37768"
## [13] "ENTREZ_GENE_ID_GSE37768"
## [14] "RefSeq.Transcript.ID_GSE37768"
## [15] "Gene.Ontology.Biological.Process_GSE37768"
## [16] "Gene.Ontology.Cellular.Component_GSE37768"
## [17] "Gene.Ontology.Molecular.Function_GSE37768"
## [18] "logFC_GSE37768"
## [19] "CI.L_GSE37768"
## [20] "CI.R_GSE37768"
## [21] "AveExpr_GSE37768"
## [22] "t_GSE37768"
## [23] "P.Value_GSE37768"
## [24] "adj.P.Val_GSE37768"
## [25] "B_GSE37768"write_csv(de4,
path=str_c(OUTPUT_DIR,"/TableGenes_",gse4,"_",TODAY,".csv")
)Time for this code chunk: 5.08631730079651
GSE47460
This experiment has the data from [Lung Tissue Research Consortium (LTRC)] (https://ltrcpublic.com/). The data is lung tissue from 582 total subjects, 254 have interstitial lung disease, 220 have COPD, and 108 are controls but they are split it in two because the authors used two different Agilent platforms Agilent-014850 Whole Human Genome Microarray and Agilent-028004 SurePrint G3 Human.
The experiment is divided in two, 429 samples in one platform and 153 in another. They dont’ have a summary table of smoking status so we can find the information using the meta data from GEO.
Raw data
Agilent raw data was provided in .txt files, and was pre processed using limma package. The following script describes the pre processing and gene expression analysis for Agilent data.
gse5<- rownames(gse_table)[5]
txt <- list.files(file.path(DATA_DIR,"celfiles",gse5))
txt <- data.frame(file=txt,X=gsub("_.*","",txt))Time for this code chunk: 0.0131757259368896
Get annotation
The annotation can be found in targets.csv file
targets<- read.csv(file.path(DATA_DIR,"celfiles",gse5,"targets.csv"))
targets <- merge(targets,txt, by = "X")
head(targets)## X disease.state.ch1 platform_id
## 1 GSM1149948 Control GPL14550
## 2 GSM1149949 Chronic Obstructive Lung Disease GPL6480
## 3 GSM1149950 Interstitial lung disease GPL14550
## 4 GSM1149951 Control GPL14550
## 5 GSM1149952 Interstitial lung disease GPL14550
## 6 GSM1149953 Chronic Obstructive Lung Disease GPL6480
## file
## 1 GSM1149948_LT000842RU_CTRL.txt
## 2 GSM1149949_LT001098RU_COPD.txt
## 3 GSM1149950_LT001600RL_ILD.txt
## 4 GSM1149951_LT001796RU_CTRL.txt
## 5 GSM1149952_LT004173LL_ILD.txt
## 6 GSM1149953_LT007392RU_COPD.txtt1 <- targets[targets$platform_id == "GPL14550",]
head(t1)## X disease.state.ch1 platform_id
## 1 GSM1149948 Control GPL14550
## 3 GSM1149950 Interstitial lung disease GPL14550
## 4 GSM1149951 Control GPL14550
## 5 GSM1149952 Interstitial lung disease GPL14550
## 11 GSM1149958 Control GPL14550
## 15 GSM1149962 Control GPL14550
## file
## 1 GSM1149948_LT000842RU_CTRL.txt
## 3 GSM1149950_LT001600RL_ILD.txt
## 4 GSM1149951_LT001796RU_CTRL.txt
## 5 GSM1149952_LT004173LL_ILD.txt
## 11 GSM1149958_LT022835RL_CTRL.txt
## 15 GSM1149962_LT026501RL_CTRL.txtTime for this code chunk: 0.0215604305267334
But we will need to download gene annotation from GEOquery
gpl <- getGEO("GPL14550")## File stored at:## /tmp/Rtmp4ZNwux/GPL14550.softgpl <- Table(gpl)
head(gpl)## ID SPOT_ID CONTROL_TYPE REFSEQ GB_ACC GENE GENE_SYMBOL
## 1 (+)E1A_r60_1 (+)E1A_r60_1 pos NA
## 2 (+)E1A_r60_3 (+)E1A_r60_3 pos NA
## 3 (+)E1A_r60_a104 (+)E1A_r60_a104 pos NA
## 4 (+)E1A_r60_a107 (+)E1A_r60_a107 pos NA
## 5 (+)E1A_r60_a135 (+)E1A_r60_a135 pos NA
## 6 (+)E1A_r60_a20 (+)E1A_r60_a20 pos NA
## GENE_NAME UNIGENE_ID ENSEMBL_ID TIGR_ID ACCESSION_STRING CHROMOSOMAL_LOCATION
## 1 NA
## 2 NA
## 3 NA
## 4 NA
## 5 NA
## 6 NA
## CYTOBAND DESCRIPTION GO_ID SEQUENCE
## 1
## 2
## 3
## 4
## 5
## 6Time for this code chunk: 3.56962299346924
Pre-process raw data
We first read files into R enviroment
dat1 = read.maimages(t1$file, path=file.path(DATA_DIR,"celfiles",gse5), source="agilent.median", green.only=T,
columns=list(G="gMedianSignal", Gb="gBGMedianSignal"),
annotation=c("Row", "Col", "ProbeName", "SystematicName")
)## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1149948_LT000842RU_CTRL.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1149950_LT001600RL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1149951_LT001796RU_CTRL.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1149952_LT004173LL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1149958_LT022835RL_CTRL.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1149962_LT026501RL_CTRL.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1149963_LT028264RL_CTRL.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1149966_LT037781RU_CTRL.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1149971_LT045714LU_CTRL.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1149975_LT047679RL_CTRL.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1149981_LT052751LL_CTRL.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1149985_LT056464LL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1149987_LT058471RU_CTRL.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1149988_LT058983RL_CTRL.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1149992_LT061842RL_CTRL.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1149993_LT063974LL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1149996_LT071706RL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1149998_LT073345RU_CTRL.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150001_LT077355RU_CTRL.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150010_LT092669LU_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150012_LT095376RL_CTRL.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150014_LT097622RU_CTRL.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150017_LT104535RL_CTRL.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150030_LT113211RL_CTRL.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150031_LT115873LU_CTRL.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150032_LT117785RL_CTRL.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150037_LT121655RL_CTRL.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150040_LT122283RM_CTRL.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150041_LT122757RU_CTRL.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150053_LT132955RU_CTRL.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150055_LT136731LU_CTRL.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150056_LT137431RL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150067_LT157253RL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150070_LT160423LL_CTRL.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150075_LT167208LL_CTRL.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150078_LT171169LU_CTRL.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150080_LT172184LU_CTRL.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150081_LT174005RL_CTRL.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150085_LT181242RM_CTRL.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150089_LT187987LL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150090_LT188012RL_CTRL.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150100_LT205601LL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150119_LT258372RL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150122_LT264690LU_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150143_LT003990RU_CTRL.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150144_LT010012LU_CTRL.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150145_LT012861RU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150146_LT023631RU_CTRL.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150147_LT030041RU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150148_LT044225RL_CTRL.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150149_LT046027RL_CTRL.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150150_LT055745RU_CTRL.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150151_LT058156LU_CTRL.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150152_LT075094LU_CTRL.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150153_LT077800RU_CTRL.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150154_LT089451RU_CTRL.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150155_LT109097LL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150156_LT115840LL_CTRL.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150157_LT120371LL_CTRL.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150158_LT132314LI_CTRL.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150159_LT141224LL_CTRL.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150160_LT141870LU_CTRL.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150161_LT148286LL_CTRL.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150162_LT155982RU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150163_LT158011LL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150164_LT159988RL_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150165_LT163513RL_CTRL.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150166_LT168204LL_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150167_LT177521RU_CTRL.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150168_LT178307RU_CTRL.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150169_LT188161LL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150170_LT194990RU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150171_LT198741LU_CTRL.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150172_LT231101RL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150173_LT231373LU_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150174_LT280560LU_CTRL.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150175_LT280646RU_CTRL.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150176_LT286300LU_CTRL.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150177_LT295133LL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150178_LT002501RL_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150179_LT002902RU_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150180_LT007259RU_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150181_LT008331RL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150182_LT017275LL_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150183_LT020259LL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150184_LT020426LU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150185_LT024106RU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150186_LT024460RU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150187_LT025997RU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150188_LT028044RU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150189_LT028427LU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150190_LT030151RU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150191_LT033422RL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150192_LT034070LU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150193_LT034821RU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150194_LT035239LU_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150195_LT035774RL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150196_LT039091RL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150197_LT041723RL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150198_LT042151RL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150199_LT042552RL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150200_LT043343LU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150201_LT043798LU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150202_LT057312LU_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150203_LT057972LU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150204_LT058691LU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150205_LT059736LU_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150206_LT059975LU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150207_LT061106RL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150208_LT062141RL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150209_LT067836RL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150210_LT070403LL_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150211_LT072387LU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150212_LT072808RL_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150213_LT075462RU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150214_LT076181LI_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150215_LT076617LL_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150216_LT077082RU_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150217_LT077317LI_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150218_LT078404RM_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150219_LT078696LU_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150220_LT080176RU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150221_LT080836RL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150222_LT082092RU_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150223_LT083706RL_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150224_LT083759RL_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150225_LT084406RU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150226_LT084808LU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150227_LT087663RL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150228_LT089723LL_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150229_LT094217RM_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150230_LT094532RL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150231_LT098394RL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150232_LT100707RL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150233_LT103266RU_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150234_LT109231RU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150235_LT111643RU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150236_LT112563LL_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150237_LT112597RU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150238_LT113005RU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150239_LT115251RU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150240_LT118064RL_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150241_LT118801RL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150242_LT122336LU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150243_LT126327LU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150244_LT130861RU_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150245_LT134279LL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150246_LT134719RU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150247_LT134829RL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150248_LT136415RU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150249_LT137832LU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150250_LT138418LU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150251_LT139051LU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150252_LT139601RU_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150253_LT140046RU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150254_LT140471RU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150255_LT148377LU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150256_LT148511LU_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150257_LT151255LL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150258_LT151920RL_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150259_LT151949RL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150260_LT152615RL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150261_LT152653LU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150262_LT154785RU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150263_LT156041LU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150264_LT156276RL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150265_LT157177RU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150266_LT158647RU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150267_LT161434RL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150268_LT162479RU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150269_LT163771RU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150270_LT165736LL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150271_LT167064RL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150272_LT168128RU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150273_LT170158LL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150274_LT175949LL_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150275_LT176562LL_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150276_LT177956LL_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150277_LT178790LL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150278_LT178929RL_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150279_LT178967RL_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150280_LT180781RL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150281_LT182636RU_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150282_LT184241RL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150283_LT184772RL_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150284_LT185970RL_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150285_LT186521RU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150286_LT188524RL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150287_LT189721RL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150288_LT190004RU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150289_LT191087RM_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150290_LT192758RU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150291_LT194473RU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150292_LT195188RL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150293_LT195871RU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150294_LT197511LU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150295_LT198062LL_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150296_LT198134LU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150297_LT198612RL_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150298_LT199384RL_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150299_LT199987LU_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150300_LT203231RM_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150301_LT203541RU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150302_LT206005RL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150303_LT208505LU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150304_LT208778RU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150305_LT210463LU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150306_LT211379RL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150307_LT212777RU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150308_LT213352LU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150309_LT213735RU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150310_LT214473RL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150311_LT216419RL_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150312_LT220968RU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150313_LT223474RM_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150314_LT228241RL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150315_LT230415RU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150316_LT233620RU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150317_LT236710RL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150318_LT238531RU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150319_LT239116RL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150321_LT242119LU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150331_LT242530RU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150332_LT243058RU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150334_LT244480LU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150336_LT245031LL_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150338_LT245084RU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150339_LT245840RL_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150341_LT246702RU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150343_LT249917LL_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150344_LT255244RU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150346_LT255718RU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150347_LT256532LL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150349_LT257433RU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150351_LT261141RU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150352_LT262371RM_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150353_LT263636RU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150354_LT266802RL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150355_LT268509LU_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150357_LT270247RU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150358_LT271100LU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150359_LT273284LL_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150361_LT277002LL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150362_LT286056RU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150363_LT287158LU_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150364_LT298520RU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150365_LT000216LL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150366_LT000379LU_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150367_LT002410RM_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150368_LT005256RL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150369_LT005419RU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150370_LT006946RM_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150371_LT011501RL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150372_LT012933RL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150373_LT013011LU_CTRL.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150374_LT017495RL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150375_LT017533RM_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150376_LT017811RM_CTRL.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150377_LT019699RL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150378_LT021461RM_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150379_LT021748RL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150380_LT022251RL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150381_LT022271LU_CTRL.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150382_LT022562LL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150383_LT024967LU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150384_LT026534RL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150385_LT030347RL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150386_LT030662LL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150387_LT036383LL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150388_LT037710RU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150389_LT041389RL_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150390_LT046539RL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150391_LT047152LL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150392_LT050079RL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150393_LT053283RU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150394_LT058319LU_CTRL.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150395_LT059721LU_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150396_LT060717LU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150397_LT062121LL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150398_LT067200LU_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150399_LT069585RL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150400_LT076421LL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150401_LT077264RU_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150402_LT078347LL_CTRL.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150403_LT079487LL_CTRL.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150404_LT080750LL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150405_LT081282RM_CTRL.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150406_LT081498RL_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150407_LT082461RL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150408_LT083950RU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150409_LT084038RM_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150410_LT087826LL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150411_LT090666LI_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150412_LT091552LL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150413_LT093297LU_CTRL.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150414_LT095342LU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150415_LT099879RL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150416_LT100821RL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150417_LT102131RL_CTRL.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150418_LT102695LL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150419_LT108067RU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150420_LT111916LL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150421_LT116004RL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150422_LT118629RL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150423_LT119682RL_CTRL.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150424_LT123131RL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150425_LT123552RL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150426_LT124161RL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150427_LT126767LL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150428_LT128191LL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150429_LT130603LL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150430_LT134121RU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150431_LT134776LL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150432_LT135390LL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150433_LT139649RU_CTRL.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150434_LT139691RU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150435_LT142159RU_CTRL.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150436_LT143944LL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150437_LT144769RL_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150438_LT145196LU_CTRL.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150439_LT147610LL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150440_LT151370RL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150441_LT151513LL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150442_LT155318RL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150443_LT156171LL_CTRL.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150444_LT156481LL_CTRL.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150445_LT157856LU_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150446_LT158795LL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150447_LT159753LL_CTRL.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150448_LT162096RL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150449_LT163384LL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150450_LT165114RU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150451_LT166111RU_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150452_LT166240RM_CTRL.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150453_LT167891LU_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150454_LT167906LL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150455_LT168094RL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150456_LT168902RM_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150457_LT171097RL_CTRL.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150458_LT172093RL_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150459_LT173597LL_CTRL.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150460_LT173946RL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150461_LT175399RU_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150462_LT176510LU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150463_LT180102RL_CTRL.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150464_LT184423LL_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150465_LT184901RL_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150466_LT185396RL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150467_LT186388LL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150468_LT190870RU_CTRL.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150469_LT191618LU_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150470_LT191675RL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150471_LT195011RL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150472_LT195207LL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150473_LT195484RU_CTRL.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150474_LT195522RU_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150475_LT196309RU_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150476_LT196677RU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150477_LT197381LI_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150478_LT197821RL_CTRL.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150479_LT200930RL_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150480_LT201348LL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150481_LT201831RU_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150482_LT204935LL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150483_LT206871LU_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150484_LT207073RU_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150485_LT213606RL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150486_LT215341RU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150487_LT220661RL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150488_LT221381RL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150489_LT221687RM_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150490_LT221782LL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150491_LT221983RM_CTRL.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150492_LT223106RU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150493_LT227135RL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150494_LT228772LL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150495_LT228897LL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150496_LT229669RU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150497_LT232073LU_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150498_LT232107RU_CTRL.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150499_LT233821RL_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150500_LT234205RL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150501_LT234755LL_CTRL.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150502_LT234774LU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150503_LT235441RL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150504_LT236045LL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150505_LT236519LL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150506_LT236557RL_CTRL.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150507_LT237439RU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150508_LT238765RL_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150509_LT241811LL_CTRL.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150510_LT242161RL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150511_LT243794RL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150512_LT244399LU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150513_LT244824RU_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150514_LT245983LU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150515_LT246349LU_CTRL.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150516_LT246774RM_CTRL.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150517_LT247728RL_CTRL.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150518_LT248906LL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150519_LT249481LU_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150520_LT249811RU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150521_LT251693LL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150522_LT251947LL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150523_LT253131RU_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150524_LT253371RL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150525_LT253677RL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150526_LT256221LL_CTRL.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150527_LT256920RU_CTRL.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150528_LT259072LU_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150529_LT264283RL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150530_LT266817LL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150531_LT270821RU_CTRL.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150532_LT277811RL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150533_LT279828RU_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150534_LT280282RL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150535_LT280851LL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150536_LT282601LL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150537_LT285031LU_CTRL.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150538_LT285671LU_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150539_LT285906LL_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150540_LT286644RL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150541_LT287196LL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150542_LT288719RL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150543_LT290677RU_CTRL.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150544_LT291449RL_COPD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150545_LT295717RL_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150546_LT297451LU_ILD.txt
## Read /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE47460/GSM1150547_LT299181LU_ILD.txtTime for this code chunk: 2.1515544851621
Then we plot raw data (in this case, I’m not evaluating this chunk because it is killing the renderization)
boxplot(dat1$E)
hist(dat1$E)Time for this code chunk: 0.000694990158081055
Normalizing data using Quantile and calculating log2 values.
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## Array 429 correcteddat1$E <- normalizeBetweenArrays(dat1$E, method="quantile")
dat1$E <- log2(dat1$E)
E = new("MAList", list(targets=dat1$targets, genes=dat1$genes, source=dat1$source, M=dat1$E, A=dat1$E))
E.avg <- avereps(E, ID=E$genes$ProbeName)Time for this code chunk: 2.68028266429901
boxplot(dat1$E)
hist(dat1$E)
Time for this code chunk: 17.652147769928
Select column with COPD description
Each experiment has its own annotation and we needed to look for a column describing which sample is a “Control” and which one is “COPD”.
Names will be different but it is important to check that “Control” group is the first level. If need it re-level groups.
t1$disease.state.ch1 <- as.factor(t1$disease.state.ch1)
t1$disease.state.ch1 <- relevel(t1$disease.state.ch1,ref = "Control")
table(t1$disease.state.ch1)##
## Control Chronic Obstructive Lung Disease
## 91 145
## Interstitial lung disease
## 193Time for this code chunk: 0.00943946838378906
Differential expression analysis
Using DE() function (described above), we performed a lineal regression model to calculate the logarithm fold change of all genes between a “Control” and a “COPD” group. We also rename colnames adding the GSE ID at the end and finally, we save the output in a .CSV file.
fit <- lmFit(E.avg$A, model.matrix(~1 + t1$disease.state.ch1))
# eBayes in lmFit model
ebf <- eBayes(fit)
print(colnames(coef(fit)))## [1] "(Intercept)"
## [2] "t1$disease.state.ch1Chronic Obstructive Lung Disease"
## [3] "t1$disease.state.ch1Interstitial lung disease"coeff ="t1$disease.state.ch1Chronic Obstructive Lung Disease"
# It gets the genes with the p-values
res <- topTable(ebf, number = Inf, p.value = 1, coef = coeff,confint=T)
volcanoplot(ebf,coef = coeff,highlight=20, pch=20)
Time for this code chunk: 2.38510274887085
Renaming columns and writing the table.
res <- merge(res,gpl,by.x=0,by.y="ID")
colnames(res) <- str_c(colnames(res),"_",gse5)
colnames(res)## [1] "Row.names_GSE47460" "logFC_GSE47460"
## [3] "CI.L_GSE47460" "CI.R_GSE47460"
## [5] "AveExpr_GSE47460" "t_GSE47460"
## [7] "P.Value_GSE47460" "adj.P.Val_GSE47460"
## [9] "B_GSE47460" "SPOT_ID_GSE47460"
## [11] "CONTROL_TYPE_GSE47460" "REFSEQ_GSE47460"
## [13] "GB_ACC_GSE47460" "GENE_GSE47460"
## [15] "GENE_SYMBOL_GSE47460" "GENE_NAME_GSE47460"
## [17] "UNIGENE_ID_GSE47460" "ENSEMBL_ID_GSE47460"
## [19] "TIGR_ID_GSE47460" "ACCESSION_STRING_GSE47460"
## [21] "CHROMOSOMAL_LOCATION_GSE47460" "CYTOBAND_GSE47460"
## [23] "DESCRIPTION_GSE47460" "GO_ID_GSE47460"
## [25] "SEQUENCE_GSE47460"write_csv(res,
path=str_c(OUTPUT_DIR,"/TableGenes_",gse5,"_",TODAY,".csv")
)Time for this code chunk: 1.09140872955322
GSE57148
The authors measured lung tissue expression from 98 COPD patients and 91 controls with normal spirometry. They are all patients with cancer nodes, smokers and male.
This experiment is RNAseq, the data will be download from Recount2. I’m following Recount2 vignette
Recount2 data
We downloaded counts from Recount2
gse6<- rownames(gse_table)[6]
## Download data from Recount2
url <- download_study('SRP041538',outdir = file.path(DATA_DIR,'SRP041538'))## 2020-07-09 08:33:43 downloading file rse_gene.Rdata to /home/ana/R-projects/Meta-analysis_COPD/data/SRP041538load(file.path(DATA_DIR, 'SRP041538', 'rse_gene.Rdata'))
## Scale counts by taking into account the total coverage per sample
rse <- scale_counts(rse_gene)
rse## class: RangedSummarizedExperiment
## dim: 58037 187
## metadata(0):
## assays(1): counts
## rownames(58037): ENSG00000000003.14 ENSG00000000005.5 ...
## ENSG00000283698.1 ENSG00000283699.1
## rowData names(3): gene_id bp_length symbol
## colnames(187): SRR1265629 SRR1265647 ... SRR1265529 SRR1265505
## colData names(21): project sample ... title characteristicsTime for this code chunk: 1.37412118911743
Get annotation
Also sample annotation is taken from Recount following its vignette.
## Sample annotation
geochar <- lapply(split(colData(rse_gene), seq_len(nrow(colData(rse_gene)))),geo_characteristics)
geochar <- do.call(rbind, lapply(geochar, function(x) {
if('cells' %in% colnames(x)) {
colnames(x)[colnames(x) == 'cells'] <- 'cell.line'
return(x)
} else {
return(x)
}
}))
table(geochar$disease.state)##
## COPD Normal
## 96 91Time for this code chunk: 1.2140953540802
Select column with COPD description
Then, we select disease.state information for calculating a differential expression analysis.
## Add sample information for DE analysis
colData(rse)$group <-factor(geochar$disease.state, levels = c("Normal","COPD"))
#write.csv(assay(rse),str_c("data/normData/","GSE57148","_normData.txt"),quote=F)Time for this code chunk: 0.0408027172088623
Differential expression analysis
Using DESeq2package we calculated DEG.
## Specify design and switch to DESeq2 format
dds <- DESeqDataSet(rse, ~ group)## converting counts to integer mode ## Perform DE analysis
dds <- DESeq(dds)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testing## -- replacing outliers and refitting for 1439 genes
## -- DESeq argument 'minReplicatesForReplace' = 7
## -- original counts are preserved in counts(dds)## estimating dispersions## fitting model and testing res <- results(dds)
# Calculates de CI
res$error <- qnorm(0.975)*res$lfcSE
res$CI.L <- res$log2FoldChange-res$error
res$CI.R <- res$log2FoldChange+res$error
res## log2 fold change (MLE): group COPD vs Normal
## Wald test p-value: group COPD vs Normal
## DataFrame with 58037 rows and 9 columns
## baseMean log2FoldChange lfcSE stat pvalue
## <numeric> <numeric> <numeric> <numeric> <numeric>
## ENSG00000000003.14 924.46223 -0.1311028 0.0763894 -1.716244 0.0861174
## ENSG00000000005.5 2.85737 -0.4396692 0.4629495 -0.949713 0.3422580
## ENSG00000000419.12 1089.25569 0.0152275 0.0300546 0.506663 0.6123911
## ENSG00000000457.13 698.75730 -0.0656469 0.0405149 -1.620314 0.1051648
## ENSG00000000460.16 347.79272 -0.0502240 0.0465180 -1.079669 0.2802896
## ... ... ... ... ... ...
## ENSG00000283695.1 0.0454355 -0.1779953 1.9047251 -0.0934493 9.25547e-01
## ENSG00000283696.1 37.5449930 0.5120456 0.0933836 5.4832482 4.17586e-08
## ENSG00000283697.1 21.2156969 0.0782234 0.1087300 0.7194274 4.71878e-01
## ENSG00000283698.1 0.1327733 -0.1294404 0.9741244 -0.1328787 8.94289e-01
## ENSG00000283699.1 0.0646975 0.1592290 1.3843041 0.1150246 9.08426e-01
## padj error CI.L CI.R
## <numeric> <numeric> <numeric> <numeric>
## ENSG00000000003.14 0.177940 0.1497204 -0.2808232 0.0186176
## ENSG00000000005.5 0.502138 0.9073642 -1.3470334 0.4676951
## ENSG00000000419.12 0.743547 0.0589059 -0.0436783 0.0741334
## ENSG00000000457.13 0.207758 0.0794078 -0.1450547 0.0137609
## ENSG00000000460.16 0.435330 0.0911736 -0.1413976 0.0409495
## ... ... ... ... ...
## ENSG00000283695.1 NA 3.733193 -3.911188 3.555197
## ENSG00000283696.1 5.80204e-07 0.183029 0.329017 0.695074
## ENSG00000283697.1 6.26366e-01 0.213107 -0.134884 0.291330
## ENSG00000283698.1 NA 1.909249 -2.038689 1.779808
## ENSG00000283699.1 NA 2.713186 -2.553957 2.872415 ## Extract Gencode gene ids
gencode <- gsub('\\..*', '', names(recount_genes))
## Find the gene information we are interested in
gene_info <- AnnotationDbi::select(org.Hs.eg.db, gencode, c('SYMBOL', 'ENSEMBL'), 'ENSEMBL')## 'select()' returned many:many mapping between keys and columnsr <- as_tibble(res, rownames="rownames")
r$rownames <- gsub("\\..*","",r$rownames)
r <- full_join(r,gene_info, by=c("rownames"="ENSEMBL"))
colnames(r) <- str_c(colnames(r),"_",gse6)
colnames(r)## [1] "rownames_GSE57148" "baseMean_GSE57148"
## [3] "log2FoldChange_GSE57148" "lfcSE_GSE57148"
## [5] "stat_GSE57148" "pvalue_GSE57148"
## [7] "padj_GSE57148" "error_GSE57148"
## [9] "CI.L_GSE57148" "CI.R_GSE57148"
## [11] "SYMBOL_GSE57148"write_csv(r,
path=str_c(OUTPUT_DIR,"/TableGenes_",gse6,"_",TODAY,".csv"))Time for this code chunk: 1.84871580998103
GSE8581
The experiment aims to find biomarkers to help in an early diagnotic. The authors used lung tissue, they had 18 smokers with nodules suspicious for lung cancer as a controls, and definded 15 COPD patients and 23 individuals that are in the middle. The criteria was COPD: COPD = FEV1<70%, FEV1/FVC<0.7 and for controls: FEV1 > 80% predicted and FEV1/FVC > 0.7.
The authors measured data using [HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array.
In GEOquery annotation, we found 19 Controls, 16 COPD and 23 Unclassifed.
Pre-process raw data
We pre-processed raw data using the function rawCEL_normCEL, plots will be shown as additional output.
gse7<- rownames(gse_table)[7]
norm7 <- rawCEL_normCEL(gse7)## Platform design info loaded.## Reading in : /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE8581/GSM210004.CEL.gz
## Reading in : /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE8581/GSM210005.CEL.gz
## Reading in : /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE8581/GSM210006.CEL.gz
## Reading in : /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE8581/GSM210007.CEL.gz
## Reading in : /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE8581/GSM210008.CEL.gz
## Reading in : /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE8581/GSM210009.CEL.gz
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## Reading in : /home/ana/R-projects/Meta-analysis_COPD/data/celfiles/GSE8581/GSM210014.CEL.gz
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## Background correcting
## Normalizing
## Calculating Expression
norm7## ExpressionSet (storageMode: lockedEnvironment)
## assayData: 54675 features, 58 samples
## element names: exprs
## protocolData
## rowNames: GSM210004.CEL.gz GSM210005.CEL.gz ... GSM213037.CEL.gz (58
## total)
## varLabels: exprs dates
## varMetadata: labelDescription channel
## phenoData
## rowNames: GSM210004.CEL.gz GSM210005.CEL.gz ... GSM213037.CEL.gz (58
## total)
## varLabels: index
## varMetadata: labelDescription channel
## featureData: none
## experimentData: use 'experimentData(object)'
## Annotation: pd.hg.u133.plus.2Time for this code chunk: 52.6767470836639
Get annotation
We used GEOquery package to obtain sample annotations and our previous calculated pre-processed values to create an ExpressionSet object.
# get annotation using GEOquery package
geo7 <- get_GEO(gse7,norm7)## Found 1 file(s)## GSE8581_series_matrix.txt.gz## Parsed with column specification:
## cols(
## .default = col_double(),
## ID_REF = col_character()
## )## See spec(...) for full column specifications.## Using locally cached version of GPL570 found here:
## /tmp/Rtmp4ZNwux/GPL570.soft## Data downloaded from GEOquery:## $GSE8581_series_matrix.txt.gz
## ExpressionSet (storageMode: lockedEnvironment)
## assayData: 54675 features, 58 samples
## element names: exprs
## protocolData: none
## phenoData
## sampleNames: GSM210004 GSM210005 ... GSM213037 (58 total)
## varLabels: title geo_accession ... Race:ch1 (46 total)
## varMetadata: labelDescription
## featureData
## featureNames: 1007_s_at 1053_at ... AFFX-TrpnX-M_at (54675 total)
## fvarLabels: ID GB_ACC ... Gene Ontology Molecular Function (16 total)
## fvarMetadata: Column Description labelDescription
## experimentData: use 'experimentData(object)'
## pubMedIds: 18849563
## Annotation: GPL570## Colnames of GEOquery object:## [1] "GSM210004" "GSM210005" "GSM210006" "GSM210007" "GSM210008"## Colnames of calculated pre-processed data:## [1] "GSM210004.CEL.gz" "GSM210005.CEL.gz" "GSM210006.CEL.gz" "GSM210007.CEL.gz"
## [5] "GSM210008.CEL.gz"Time for this code chunk: 12.3359503746033
Select column with COPD description
Each experiment has its own annotation and we needed to look for a column describing which sample is a “Control” and which one is “COPD”.
head(pData(geo7))## title geo_accession status
## GSM210004 Human_COPD_Case1 GSM210004 Public on May 31 2008
## GSM210005 Human_Control1 GSM210005 Public on May 31 2008
## GSM210006 Human_COPD_Case2 GSM210006 Public on May 31 2008
## GSM210007 Human_Lung_Unclassified1 GSM210007 Public on May 31 2008
## GSM210008 Human_COPD_Case3 GSM210008 Public on May 31 2008
## GSM210009 Human_Control2 GSM210009 Public on May 31 2008
## submission_date last_update_date type channel_count source_name_ch1
## GSM210004 Jul 12 2007 Aug 28 2018 RNA 1 Whole lung
## GSM210005 Jul 12 2007 Aug 28 2018 RNA 1 Whole Lung
## GSM210006 Jul 12 2007 Aug 28 2018 RNA 1 Whole Lung
## GSM210007 Jul 12 2007 Aug 28 2018 RNA 1 Whole Lung
## GSM210008 Jul 12 2007 Aug 28 2018 RNA 1 Whole Lung
## GSM210009 Jul 12 2007 Aug 28 2018 RNA 1 Whole Lung
## organism_ch1
## GSM210004 Homo sapiens
## GSM210005 Homo sapiens
## GSM210006 Homo sapiens
## GSM210007 Homo sapiens
## GSM210008 Homo sapiens
## GSM210009 Homo sapiens
## characteristics_ch1
## GSM210004 Race: Caucasian, Age: 63, Gender: Male, Height: 72in.
## GSM210005 Race: AfricanAmerican, Gender: Female, Age: 84, Height: 60in.
## GSM210006 Race: AfricanAmerican, Gender: Female, Age: 65, Height: 66in.
## GSM210007 Race: Caucasian, Age: 46, Gender: Male, Height: 66in.
## GSM210008 Race: Caucasian, Age: 53, Gender: Female, Height: 65in.
## GSM210009 Race: Caucasian, Age: 60, Gender: Female, Height: 64in.
## characteristics_ch1.1 biomaterial_provider_ch1 molecule_ch1
## GSM210004 Mariani Lab total RNA
## GSM210005 Mariani Lab total RNA
## GSM210006 Mariani Lab total RNA
## GSM210007 Mariani Lab total RNA
## GSM210008 Mariani Lab total RNA
## GSM210009 Mariani Lab total RNA
## extract_protocol_ch1
## GSM210004 For each experimental sample, RNA quality was assessed by RNA Nano LabChip analysis on an Agilent Bioanalyzer 2100. Concentrations may also be determined using a NanoDrop 1000 spectrophotometer. Under standard conditions processing of RNAs for GeneChip Analysis was in accordance with methods described in the Affymetrix GeneChip Expression Analysis Technical Manual, revision four, as subsequently detailed. Synthesis of cDNA first and second strand is performed using the GeneChip Expression 3’-Amplification Reagents One-Cycle cDNA Synthesis Kit (P/N 900431). Cleanup of the double stranded product is carried according to standard Affymetrix protocols using the Affymetrix GeneChip Cleanup Module (Affymetrix Catalog # 900371).
## GSM210005 For each experimental sample, RNA quality was assessed by RNA Nano LabChip analysis on an Agilent Bioanalyzer 2100. Concentrations may also be determined using a NanoDrop 1000 spectrophotometer. Under standard conditions processing of RNAs for GeneChip Analysis was in accordance with methods described in the Affymetrix GeneChip Expression Analysis Technical Manual, revision four, as subsequently detailed. Synthesis of cDNA first and second strand is performed using the GeneChip Expression 3’-Amplification Reagents One-Cycle cDNA Synthesis Kit (P/N 900431). Cleanup of the double stranded product is carried according to standard Affymetrix protocols using the Affymetrix GeneChip Cleanup Module (Affymetrix Catalog # 900371).
## GSM210006 For each experimental sample, RNA quality was assessed by RNA Nano LabChip analysis on an Agilent Bioanalyzer 2100. Concentrations may also be determined using a NanoDrop 1000 spectrophotometer. Under standard conditions processing of RNAs for GeneChip Analysis was in accordance with methods described in the Affymetrix GeneChip Expression Analysis Technical Manual, revision four, as subsequently detailed. Synthesis of cDNA first and second strand is performed using the GeneChip Expression 3’-Amplification Reagents One-Cycle cDNA Synthesis Kit (P/N 900431). Cleanup of the double stranded product is carried according to standard Affymetrix protocols using the Affymetrix GeneChip Cleanup Module (Affymetrix Catalog # 900371).
## GSM210007 For each experimental sample, RNA quality was assessed by RNA Nano LabChip analysis on an Agilent Bioanalyzer 2100. Concentrations may also be determined using a NanoDrop 1000 spectrophotometer. Under standard conditions processing of RNAs for GeneChip Analysis was in accordance with methods described in the Affymetrix GeneChip Expression Analysis Technical Manual, revision four, as subsequently detailed. Synthesis of cDNA first and second strand is performed using the GeneChip Expression 3’-Amplification Reagents One-Cycle cDNA Synthesis Kit (P/N 900431). Cleanup of the double stranded product is carried according to standard Affymetrix protocols using the Affymetrix GeneChip Cleanup Module (Affymetrix Catalog # 900371).
## GSM210008 For each experimental sample, RNA quality was assessed by RNA Nano LabChip analysis on an Agilent Bioanalyzer 2100. Concentrations may also be determined using a NanoDrop 1000 spectrophotometer. Under standard conditions processing of RNAs for GeneChip Analysis was in accordance with methods described in the Affymetrix GeneChip Expression Analysis Technical Manual, revision four, as subsequently detailed. Synthesis of cDNA first and second strand is performed using the GeneChip Expression 3’-Amplification Reagents One-Cycle cDNA Synthesis Kit (P/N 900431). Cleanup of the double stranded product is carried according to standard Affymetrix protocols using the Affymetrix GeneChip Cleanup Module (Affymetrix Catalog # 900371).
## GSM210009 For each experimental sample, RNA quality was assessed by RNA Nano LabChip analysis on an Agilent Bioanalyzer 2100. Concentrations may also be determined using a NanoDrop 1000 spectrophotometer. Under standard conditions processing of RNAs for GeneChip Analysis was in accordance with methods described in the Affymetrix GeneChip Expression Analysis Technical Manual, revision four, as subsequently detailed. Synthesis of cDNA first and second strand is performed using the GeneChip Expression 3’-Amplification Reagents One-Cycle cDNA Synthesis Kit (P/N 900431). Cleanup of the double stranded product is carried according to standard Affymetrix protocols using the Affymetrix GeneChip Cleanup Module (Affymetrix Catalog # 900371).
## label_ch1
## GSM210004 Biotin
## GSM210005 Biotin
## GSM210006 Biotin
## GSM210007 Biotin
## GSM210008 Biotin
## GSM210009 Biotin
## label_protocol_ch1
## GSM210004 In vitro transcription (IVT) is performed using the GeneChip Expression Amplification Reagents kit- 30 reactions (P/N 900449) and is carried out according to the standard Affymetrix protocols and quantification of the IVT samples is carried out on a Bio-Tek UV Plate Reader.
## GSM210005 In vitro transcription (IVT) is performed using the GeneChip Expression Amplification Reagents kit- 30 reactions (P/N 900449) and is carried out according to the standard Affymetrix protocols and quantification of the IVT samples is carried out on a Bio-Tek UV Plate Reader
## GSM210006 In vitro transcription (IVT) is performed using the GeneChip Expression Amplification Reagents kit- 30 reactions (P/N 900449) and is carried out according to the standard Affymetrix protocols and quantification of the IVT samples is carried out on a Bio-Tek UV Plate Reader.
## GSM210007 In vitro transcription (IVT) is performed using the GeneChip Expression Amplification Reagents kit- 30 reactions (P/N 900449) and is carried out according to the standard Affymetrix protocols and quantification of the IVT samples is carried out on a Bio-Tek UV Plate Reader.
## GSM210008 In vitro transcription (IVT) is performed using the GeneChip Expression Amplification Reagents kit- 30 reactions (P/N 900449) and is carried out according to the standard Affymetrix protocols and quantification of the IVT samples is carried out on a Bio-Tek UV Plate Reader.
## GSM210009 In vitro transcription (IVT) is performed using the GeneChip Expression Amplification Reagents kit- 30 reactions (P/N 900449) and is carried out according to the standard Affymetrix protocols and quantification of the IVT samples is carried out on a Bio-Tek UV Plate Reader.
## taxid_ch1
## GSM210004 9606
## GSM210005 9606
## GSM210006 9606
## GSM210007 9606
## GSM210008 9606
## GSM210009 9606
## hyb_protocol
## GSM210004 Hybridization is carried out according the Affymetrix GeneChip® Manual. Twenty micrograms of IVT material is the nominal amount used on the GeneChip® arrays. Affymetrix hybridization ovens are used to incubate the arrays at a constant temperature of 45oC overnight.
## GSM210005 Hybridization is carried out according the Affymetrix GeneChip® Manual. Twenty micrograms of IVT material is the nominal amount used on the GeneChip® arrays. Affymetrix hybridization ovens are used to incubate the arrays at a constant temperature of 45oC overnight.
## GSM210006 Hybridization is carried out according the Affymetrix GeneChip® Manual. Twenty micrograms of IVT material is the nominal amount used on the GeneChip® arrays. Affymetrix hybridization ovens are used to incubate the arrays at a constant temperature of 45oC overnight.
## GSM210007 Hybridization is carried out according the Affymetrix GeneChip® Manual. Twenty micrograms of IVT material is the nominal amount used on the GeneChip® arrays. Affymetrix hybridization ovens are used to incubate the arrays at a constant temperature of 45oC overnight.
## GSM210008 Hybridization is carried out according the Affymetrix GeneChip® Manual. Twenty micrograms of IVT material is the nominal amount used on the GeneChip® arrays. Affymetrix hybridization ovens are used to incubate the arrays at a constant temperature of 45oC overnight.
## GSM210009 Hybridization is carried out according the Affymetrix GeneChip® Manual. Twenty micrograms of IVT material is the nominal amount used on the GeneChip® arrays. Affymetrix hybridization ovens are used to incubate the arrays at a constant temperature of 45oC overnight.
## hyb_protocol.1
## GSM210004 Preparation of microarrays for scanning is carried out with Affymetrix appropriate wash protocols matched to the specific chip type on a Model 450 Fluidics station. Affymetrix GeneChip Operating Software (GCOS) operating system controls the Fluidics station process.
## GSM210005 Preparation of microarrays for scanning is carried out with Affymetrix appropriate wash protocols matched to the specific chip type on a Model 450 Fluidics station. Affymetrix GeneChip Operating Software (GCOS) operating system controls the Fluidics station process.
## GSM210006 Preparation of microarrays for scanning is carried out with Affymetrix appropriate wash protocols matched to the specific chip type on a Model 450 Fluidics station. Affymetrix GeneChip Operating Software (GCOS) operating system controls the Fluidics station process.
## GSM210007 Preparation of microarrays for scanning is carried out with Affymetrix appropriate wash protocols matched to the specific chip type on a Model 450 Fluidics station. Affymetrix GeneChip Operating Software (GCOS) operating system controls the Fluidics station process.
## GSM210008 Preparation of microarrays for scanning is carried out with Affymetrix appropriate wash protocols matched to the specific chip type on a Model 450 Fluidics station. Affymetrix GeneChip Operating Software (GCOS) operating system controls the Fluidics station process.
## GSM210009 Preparation of microarrays for scanning is carried out with Affymetrix appropriate wash protocols matched to the specific chip type on a Model 450 Fluidics station. Affymetrix GeneChip Operating Software (GCOS) operating system controls the Fluidics station process.
## scan_protocol
## GSM210004 Scanning is carried out on a GeneChip® Scanner 3000 7G scanner with autoloader. The Affymetrix GCOS v1.3 operating system controls the Model 3000 7G scanner and data acquisition functions. GCOS maintains the mediated first level data analysis and desktop data management for the entire GeneChip System. Chip library files specific to each array and necessary for scan interpretation are stored on the computer workstation controlling the scanner and are updated regularly as necessary when updates are made available from Affymetrix. Collected research data is stored on the hard drive of the instrument computer, transferred to a mirrored storage disk and to a raid 5 server operating on the Partners Healthcare network and backed up nightly via the Partners Healthcare Systems IT backup utility. All systems are CFR21-11 and HIPPA compliant
## GSM210005 Scanning is carried out on a GeneChip® Scanner 3000 7G scanner with autoloader. The Affymetrix GCOS v1.3 operating system controls the Model 3000 7G scanner and data acquisition functions. GCOS maintains the mediated first level data analysis and desktop data management for the entire GeneChip System. Chip library files specific to each array and necessary for scan interpretation are stored on the computer workstation controlling the scanner and are updated regularly as necessary when updates are made available from Affymetrix. Collected research data is stored on the hard drive of the instrument computer, transferred to a mirrored storage disk and to a raid 5 server operating on the Partners Healthcare network and backed up nightly via the Partners Healthcare Systems IT backup utility. All systems are CFR21-11 and HIPPA compliant
## GSM210006 Scanning is carried out on a GeneChip® Scanner 3000 7G scanner with autoloader. The Affymetrix GCOS v1.3 operating system controls the Model 3000 7G scanner and data acquisition functions. GCOS maintains the mediated first level data analysis and desktop data management for the entire GeneChip System. Chip library files specific to each array and necessary for scan interpretation are stored on the computer workstation controlling the scanner and are updated regularly as necessary when updates are made available from Affymetrix. Collected research data is stored on the hard drive of the instrument computer, transferred to a mirrored storage disk and to a raid 5 server operating on the Partners Healthcare network and backed up nightly via the Partners Healthcare Systems IT backup utility. All systems are CFR21-11 and HIPPA compliant
## GSM210007 Scanning is carried out on a GeneChip® Scanner 3000 7G scanner with autoloader. The Affymetrix GCOS v1.3 operating system controls the Model 3000 7G scanner and data acquisition functions. GCOS maintains the mediated first level data analysis and desktop data management for the entire GeneChip System. Chip library files specific to each array and necessary for scan interpretation are stored on the computer workstation controlling the scanner and are updated regularly as necessary when updates are made available from Affymetrix. Collected research data is stored on the hard drive of the instrument computer, transferred to a mirrored storage disk and to a raid 5 server operating on the Partners Healthcare network and backed up nightly via the Partners Healthcare Systems IT backup utility. All systems are CFR21-11 and HIPPA compliant
## GSM210008 Scanning is carried out on a GeneChip® Scanner 3000 7G scanner with autoloader. The Affymetrix GCOS v1.3 operating system controls the Model 3000 7G scanner and data acquisition functions. GCOS maintains the mediated first level data analysis and desktop data management for the entire GeneChip System. Chip library files specific to each array and necessary for scan interpretation are stored on the computer workstation controlling the scanner and are updated regularly as necessary when updates are made available from Affymetrix. Collected research data is stored on the hard drive of the instrument computer, transferred to a mirrored storage disk and to a raid 5 server operating on the Partners Healthcare network and backed up nightly via the Partners Healthcare Systems IT backup utility. All systems are CFR21-11 and HIPPA compliant
## GSM210009 Scanning is carried out on a GeneChip® Scanner 3000 7G scanner with autoloader. The Affymetrix GCOS v1.3 operating system controls the Model 3000 7G scanner and data acquisition functions. GCOS maintains the mediated first level data analysis and desktop data management for the entire GeneChip System. Chip library files specific to each array and necessary for scan interpretation are stored on the computer workstation controlling the scanner and are updated regularly as necessary when updates are made available from Affymetrix. Collected research data is stored on the hard drive of the instrument computer, transferred to a mirrored storage disk and to a raid 5 server operating on the Partners Healthcare network and backed up nightly via the Partners Healthcare Systems IT backup utility. All systems are CFR21-11 and HIPPA compliant
## scan_protocol.1
## GSM210004 Please take note: this material is available only for use in support of grant applications or publications by PHS investigators intending to use or using the HPCGG services. Any other use of this material without express authorization is prohibited.
## GSM210005 Please take note: this material is available only for use in support of grant applications or publications by PHS investigators intending to use or using the HPCGG services. Any other use of this material without express authorization is prohibited.
## GSM210006 Please take note: this material is available only for use in support of grant applications or publications by PHS investigators intending to use or using the HPCGG services. Any other use of this material without express authorization is prohibited.
## GSM210007 Please take note: this material is available only for use in support of grant applications or publications by PHS investigators intending to use or using the HPCGG services. Any other use of this material without express authorization is prohibited.
## GSM210008 Please take note: this material is available only for use in support of grant applications or publications by PHS investigators intending to use or using the HPCGG services. Any other use of this material without express authorization is prohibited.
## GSM210009
## scan_protocol.2
## GSM210004 HPCGG Operations, 2006.
## GSM210005 HPCGG Operations, 2006.
## GSM210006 HPCGG Operations, 2006.
## GSM210007 HPCGG Operations, 2006.
## GSM210008 HPCGG Operations, 2006.
## GSM210009
## description
## GSM210004 FEV1:2.54, Ratio:58, Diagnosis: NSC Squamous, ArrayID: 610A
## GSM210005 FEV1:1.69, Ratio:83.66, Diagnosis: NSC Adeno, ArrayID: 610B
## GSM210006 FEV1:1.41, Ratio:51, Diagnosis: NSC Adeno, ArrayID: 610C
## GSM210007 FEV1:2.51, Ratio:80.96, Diagnosis: NSC Adeno, ArrayID: 610D
## GSM210008 FEV1:1.64, Ratio:57, Diagnosis: NSC Adeno, ArrayId: 610E
## GSM210009 FEV1:2.72, Ratio:74, Diagnosis: NSC Squamo, ArrayID: 610F
## description.1 data_processing platform_id contact_name
## GSM210004 Mas5 GPL570 Soumyaroop,,Bhattacharya
## GSM210005 Mas5 GPL570 Soumyaroop,,Bhattacharya
## GSM210006 Mas5 GPL570 Soumyaroop,,Bhattacharya
## GSM210007 Mas5 GPL570 Soumyaroop,,Bhattacharya
## GSM210008 Mas5 GPL570 Soumyaroop,,Bhattacharya
## GSM210009 Mas5 GPL570 Soumyaroop,,Bhattacharya
## contact_email contact_phone contact_fax
## GSM210004 Soumyaroop_Bhattacharya@URMC.rochester.edu 585-276-4683 585-276-2642
## GSM210005 Soumyaroop_Bhattacharya@URMC.rochester.edu 585-276-4683 585-276-2642
## GSM210006 Soumyaroop_Bhattacharya@URMC.rochester.edu 585-276-4683 585-276-2642
## GSM210007 Soumyaroop_Bhattacharya@URMC.rochester.edu 585-276-4683 585-276-2642
## GSM210008 Soumyaroop_Bhattacharya@URMC.rochester.edu 585-276-4683 585-276-2642
## GSM210009 Soumyaroop_Bhattacharya@URMC.rochester.edu 585-276-4683 585-276-2642
## contact_laboratory contact_department
## GSM210004 Mariani Pediatrics
## GSM210005 Mariani Pediatrics
## GSM210006 Mariani Pediatrics
## GSM210007 Mariani Pediatrics
## GSM210008 Mariani Pediatrics
## GSM210009 Mariani Pediatrics
## contact_institute contact_address
## GSM210004 University of Rochester Medical Center 601 Elmwood Avenue Box 850
## GSM210005 University of Rochester Medical Center 601 Elmwood Avenue Box 850
## GSM210006 University of Rochester Medical Center 601 Elmwood Avenue Box 850
## GSM210007 University of Rochester Medical Center 601 Elmwood Avenue Box 850
## GSM210008 University of Rochester Medical Center 601 Elmwood Avenue Box 850
## GSM210009 University of Rochester Medical Center 601 Elmwood Avenue Box 850
## contact_city contact_state contact_zip/postal_code contact_country
## GSM210004 Rochester NY 14642 USA
## GSM210005 Rochester NY 14642 USA
## GSM210006 Rochester NY 14642 USA
## GSM210007 Rochester NY 14642 USA
## GSM210008 Rochester NY 14642 USA
## GSM210009 Rochester NY 14642 USA
## contact_web_link
## GSM210004 http://lungtranscriptome.bwh.harvard.edu/
## GSM210005 http://lungtranscriptome.bwh.harvard.edu/
## GSM210006 http://lungtranscriptome.bwh.harvard.edu/
## GSM210007 http://lungtranscriptome.bwh.harvard.edu/
## GSM210008 http://lungtranscriptome.bwh.harvard.edu/
## GSM210009 http://lungtranscriptome.bwh.harvard.edu/
## supplementary_file
## GSM210004 ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM210nnn/GSM210004/suppl/GSM210004.CEL.gz
## GSM210005 ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM210nnn/GSM210005/suppl/GSM210005.CEL.gz
## GSM210006 ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM210nnn/GSM210006/suppl/GSM210006.CEL.gz
## GSM210007 ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM210nnn/GSM210007/suppl/GSM210007.CEL.gz
## GSM210008 ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM210nnn/GSM210008/suppl/GSM210008.CEL.gz
## GSM210009 ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM210nnn/GSM210009/suppl/GSM210009.CEL.gz
## supplementary_file.1
## GSM210004 ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM210nnn/GSM210004/suppl/GSM210004.CHP.gz
## GSM210005 ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM210nnn/GSM210005/suppl/GSM210005.CHP.gz
## GSM210006 ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM210nnn/GSM210006/suppl/GSM210006.CHP.gz
## GSM210007 ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM210nnn/GSM210007/suppl/GSM210007.CHP.gz
## GSM210008 ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM210nnn/GSM210008/suppl/GSM210008.CHP.gz
## GSM210009 ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM210nnn/GSM210009/suppl/GSM210009.CHP.gz
## data_row_count relation relation.1
## GSM210004 54675 Reanalyzed by: GSE60486 Reanalyzed by: GSE119087
## GSM210005 54675 Reanalyzed by: GSE60486 Reanalyzed by: GSE119087
## GSM210006 54675 Reanalyzed by: GSE60486 Reanalyzed by: GSE119087
## GSM210007 54675 Reanalyzed by: GSE119087
## GSM210008 54675 Reanalyzed by: GSE60486 Reanalyzed by: GSE119087
## GSM210009 54675 Reanalyzed by: GSE60486 Reanalyzed by: GSE119087
## Age:ch1 Race:ch1
## GSM210004 <NA> Caucasian, Age: 63, Gender: Male, Height: 72in.
## GSM210005 <NA> AfricanAmerican, Gender: Female, Age: 84, Height: 60in.
## GSM210006 <NA> AfricanAmerican, Gender: Female, Age: 65, Height: 66in.
## GSM210007 <NA> Caucasian, Age: 46, Gender: Male, Height: 66in.
## GSM210008 <NA> Caucasian, Age: 53, Gender: Female, Height: 65in.
## GSM210009 <NA> Caucasian, Age: 60, Gender: Female, Height: 64in.Time for this code chunk: 0.0283792018890381
Names will be different but it is important to check that “Control” group is the first level. If need it re-level groups.
pData(geo7)["Disease"] <- factor(str_remove_all(pData(geo7)[,"title"], "[0-9]"))
table(pData(geo7)$Disease)##
## Human_Control Human_Controla Human_COPD_Case
## 18 1 15
## Human_COPD_CaseX Human_Lung_Unclassified
## 1 23Time for this code chunk: 0.104738712310791
Differential expression analysis
Using DE() function (described above), we performed a lineal regression model to calculate the logarithm fold change of all genes between a “Control” and a “COPD” group. We also rename colnames adding the GSE ID at the end and finally, we save the output in a .CSV file.
de7 <- DE(geo7,coeff = 3)## [1] "(Intercept)" "DiseaseHuman_Controla"
## [3] "DiseaseHuman_COPD_Case" "DiseaseHuman_COPD_CaseX"
## [5] "DiseaseHuman_Lung_Unclassified"
colnames(de7) <- str_c(colnames(de7),"_",gse7)
colnames(de7)## [1] "rownames_GSE8581"
## [2] "ID_GSE8581"
## [3] "GB_ACC_GSE8581"
## [4] "SPOT_ID_GSE8581"
## [5] "Species.Scientific.Name_GSE8581"
## [6] "Annotation.Date_GSE8581"
## [7] "Sequence.Type_GSE8581"
## [8] "Sequence.Source_GSE8581"
## [9] "Target.Description_GSE8581"
## [10] "Representative.Public.ID_GSE8581"
## [11] "Gene.Title_GSE8581"
## [12] "Gene.Symbol_GSE8581"
## [13] "ENTREZ_GENE_ID_GSE8581"
## [14] "RefSeq.Transcript.ID_GSE8581"
## [15] "Gene.Ontology.Biological.Process_GSE8581"
## [16] "Gene.Ontology.Cellular.Component_GSE8581"
## [17] "Gene.Ontology.Molecular.Function_GSE8581"
## [18] "logFC_GSE8581"
## [19] "CI.L_GSE8581"
## [20] "CI.R_GSE8581"
## [21] "AveExpr_GSE8581"
## [22] "t_GSE8581"
## [23] "P.Value_GSE8581"
## [24] "adj.P.Val_GSE8581"
## [25] "B_GSE8581"write_csv(de7,
path=str_c(OUTPUT_DIR,"/TableGenes_",gse7,"_",TODAY,".csv")
)Time for this code chunk: 3.5228488445282
Output
This script produces the following data, and can be found in /home/ana/R-projects/Meta-analysis_COPD
Tables with DE results: Tables with log fold change and p-values calculated
Table of merged results: Table with all DE results
Session Info
sessionInfo()## R version 4.0.2 (2020-06-22)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/liblapack.so.3
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=it_IT.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=it_IT.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=it_IT.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=it_IT.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats4 parallel stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] pd.hg.u133.plus.2_3.12.0 pd.huex.1.0.st.v2_3.14.1
## [3] pd.hg.u133a_3.12.0 pd.hu6800_3.12.0
## [5] DBI_1.1.0 RSQLite_2.2.0
## [7] recount_1.14.0 org.Hs.eg.db_3.11.4
## [9] AnnotationDbi_1.50.1 DESeq2_1.28.1
## [11] GEOquery_2.56.0 SummarizedExperiment_1.18.1
## [13] DelayedArray_0.14.0 matrixStats_0.56.0
## [15] GenomicRanges_1.40.0 GenomeInfoDb_1.24.2
## [17] limma_3.44.3 forcats_0.5.0
## [19] stringr_1.4.0 dplyr_1.0.0
## [21] purrr_0.3.4 readr_1.3.1
## [23] tidyr_1.1.0 tibble_3.0.2
## [25] ggplot2_3.3.2 tidyverse_1.3.0
## [27] oligo_1.52.0 Biostrings_2.56.0
## [29] XVector_0.28.0 IRanges_2.22.2
## [31] S4Vectors_0.26.1 Biobase_2.48.0
## [33] oligoClasses_1.50.0 BiocGenerics_0.34.0
## [35] knitr_1.29
##
## loaded via a namespace (and not attached):
## [1] readxl_1.3.1 backports_1.1.8 Hmisc_4.4-0
## [4] BiocFileCache_1.12.0 plyr_1.8.6 splines_4.0.2
## [7] BiocParallel_1.22.0 digest_0.6.25 foreach_1.5.0
## [10] htmltools_0.5.0 fansi_0.4.1 magrittr_1.5
## [13] checkmate_2.0.0 memoise_1.1.0 BSgenome_1.56.0
## [16] cluster_2.1.0 annotate_1.66.0 modelr_0.1.8
## [19] askpass_1.1 prettyunits_1.1.1 jpeg_0.1-8.1
## [22] colorspace_1.4-1 blob_1.2.1 rvest_0.3.5
## [25] rappdirs_0.3.1 haven_2.3.1 xfun_0.15
## [28] crayon_1.3.4 RCurl_1.98-1.2 jsonlite_1.7.0
## [31] genefilter_1.70.0 survival_3.2-3 VariantAnnotation_1.34.0
## [34] iterators_1.0.12 glue_1.4.1 gtable_0.3.0
## [37] zlibbioc_1.34.0 rentrez_1.2.2 scales_1.1.1
## [40] rngtools_1.5 derfinderHelper_1.22.0 derfinder_1.22.0
## [43] Rcpp_1.0.5 xtable_1.8-4 progress_1.2.2
## [46] htmlTable_2.0.1 bumphunter_1.30.0 foreign_0.8-80
## [49] bit_1.1-15.2 preprocessCore_1.50.0 Formula_1.2-3
## [52] htmlwidgets_1.5.1 httr_1.4.1 RColorBrewer_1.1-2
## [55] acepack_1.4.1 ellipsis_0.3.1 ff_2.2-14.2
## [58] pkgconfig_2.0.3 XML_3.99-0.4 nnet_7.3-14
## [61] dbplyr_1.4.4 locfit_1.5-9.4 reshape2_1.4.4
## [64] tidyselect_1.1.0 rlang_0.4.6 munsell_0.5.0
## [67] cellranger_1.1.0 tools_4.0.2 cli_2.0.2
## [70] downloader_0.4 generics_0.0.2 broom_0.5.6
## [73] evaluate_0.14 yaml_2.2.1 bit64_0.9-7
## [76] fs_1.4.2 doRNG_1.8.2 nlme_3.1-148
## [79] xml2_1.3.2 biomaRt_2.44.1 BiocStyle_2.16.0
## [82] compiler_4.0.2 rstudioapi_0.11 curl_4.3
## [85] png_0.1-7 affyio_1.58.0 reprex_0.3.0
## [88] geneplotter_1.66.0 stringi_1.4.6 highr_0.8
## [91] GenomicFeatures_1.40.0 GenomicFiles_1.24.0 lattice_0.20-41
## [94] Matrix_1.2-18 vctrs_0.3.1 pillar_1.4.4
## [97] lifecycle_0.2.0 BiocManager_1.30.10 data.table_1.12.8
## [100] bitops_1.0-6 qvalue_2.20.0 rtracklayer_1.48.0
## [103] R6_2.4.1 latticeExtra_0.6-29 gridExtra_2.3
## [106] affxparser_1.60.0 codetools_0.2-16 assertthat_0.2.1
## [109] openssl_1.4.2 withr_2.2.0 GenomicAlignments_1.24.0
## [112] Rsamtools_2.4.0 GenomeInfoDbData_1.2.3 hms_0.5.3
## [115] grid_4.0.2 rpart_4.1-15 rmarkdown_2.3
## [118] lubridate_1.7.9 base64enc_0.1-3Time for this code chunk: 0.0573010444641113